Impact case study database
Search and filter
Filter by
- University of Ulster
- 11 - Computer Science and Informatics
- Submitting institution
- University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Summary impact type
- Health
- Is this case study continued from a case study submitted in 2014?
- No
1. Summary of the impact
Ulster has significantly improved patient outcomes, stimulated the UK neurotechnology sector and promoted knowledge exchange arising from the University’s AI for brain-computer interface (BCI) research. Impacts include:
I1 – establishing non-subjective evidence of consciousness in brain-injured patients with prolonged disorders of consciousness with specific beneficial, life-changing outcomes and leading to a national neurotechnology trial with 17 hospitals
I2 – influencing two medico-legal High Court cases resulting in substantial compensation for brain-injured victims
I3 – enabling a spinal-injured person to compete against other teams from around the world as a Cybathlete at Cybathlon 2016, 2019 (BCI series) and 2020
I4 – creation of a spinout company, NeuroCONCISE Ltd (Nov 2016), employing 5 people, to commercialise Ulster BCI research
I5 – informing national reports that influenced the establishment of the KTN Neurotechnology Innovation Network and the UK roadmap for neurotechnology
2. Underpinning research
Since 2003 neurotechnology and brain-computer interface (BCI) R&D at Ulster has led to the development of advanced award-winning algorithms, software and hardware for translating brain activity recorded non-invasively (electroencephalography (EEG)) into control signals to enable people to communicate and interact with technology without moving and to assess awareness/consciousness following brain injury, as evidenced in [C1-C9]. Significant research advances that underpin the impacts are clustered in three main topics and mapped to references [ R1-R6] and the specific impacts [ I1-I5]:
R1 - R3 - Design of award-winning AI and algorithms to translate brain activity into control signals [I1-I5]: Advanced EEG-based signal processing framework called Neural-Time-Series-Prediction-Preprocessing (NTSPP) involving multiple neural networks and/or self-organising fuzzy neural networks (SOFNN) which enhance the separability of signals recorded while a BCI user performs motor imagery (imagined movement). NTSPP is combined with other machine learning approaches to maximise classification accuracy in low signal to noise situations. The NTSPP framework produces a surrogate data space which is more separable through neural network based specialization in time-series prediction of individual EEG channels/sensors and for different mental tasks (e.g., imagined left vs right hand movement). NTSPP has been extended to include multiple time-series, multiple classes and integrated with a range of other signal processing techniques. It has also been shown that subject(BCI user)-specific time-embedding of the time-series increases network specialization to improve BCI performance as spatially disparate EEG channels have different optimal time embedding parameters which change and evolve depending on the brain signal being processed. With self-organising fuzzy neural networks the NTSSP can be trained easily and deployed to decode and translate brain signals into control signals in real time.
R4 - Design of advanced neurofeedback technologies for disabled users [I1-I5]:
Motor imagery to modulate sensorimotor rhythms (SMR) that are classified in BCI technologies is a skill that can be learned but very much depends on the type of neurofeedback the user receives. Auditory [R4] and visual (games) [R5] can be used. Novel visual feedback modalities have been presented and analysed on able-bodied and spinal-injured research participants in [R5] (co-authored with clinical collaborators and now in NeuroCONCISE Products). A novel auditory feedback paradigm, presented in [R6] with patients (co-authored with clinical collaborators and now in NeuroCONCISE Products) was proposed and trialled on able-bodied users in [R4].
R5-R6 - Technology trials with patients [I1I-5]. In [R6] to date Ulster has conducted BCI research with 25 patients who have suffered a traumatic brain injury (TBI) resulting in prolonged disorders of consciousness (PDoC). The initial study in [R6] describes work with four patients and had three main aims: 1) showing, for the first time, results of motor imagery feedback to a patient with minimally conscious state (MCS) and reporting on how this could influence a detection of awareness/consciousness protocol involving BCI, allowing the participant (PDoC patient) to experience control of something external from the body as opposed to BCI protocol that involved no feedback. All EEG-based awareness detection studies prior to our research did not provide real-time feedback to the patient during the assessment; 2) As many PDoC patients have limited eye gaze control visual feedback modalities for motor imagery are often not suitable. Our research involved auditory feedback [R4] of sensorimotor activity allowing the user to hear the target and listen to the feedback even when eye gaze control was not possible; and 3) we used musical auditory feedback in the form of a palette of different musical genres to improve the experience for the user. Using music feedback allowed us to engage with the patients and their care teams/families to enliven the experimental conditions for this challenging patient group. No other research study had trialled or demonstrated the impact of this approach.
3. References to the research
Outputs can be provided by Ulster University on request.
[R1] D. Coyle, G. Prasad, and T. M. McGinnity, “A time-series prediction approach for feature extraction in a brain-computer interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 4, pp. 461–7, Dec. 2005.
[R2] D. Coyle, “Neural network-based auto association and time-series prediction for biosignal processing in brain-computer interfaces,” IEEE Computational Intelligence Magazine, vol. 4, no. 4, pp. 47–59, Nov. 2009.
[R3] D. Coyle, G. Prasad, and T. M. McGinnity, “Faster self-organizing fuzzy neural network training and a hyperparameter analysis for a brain-computer interface.,” IEEE Transactions on Systems Man and Cybernetics. B. Cybernetics, vol. 39, no. 6, pp. 1458–71, Dec. 2009.
[R4] K. A. McCreadie, D. H. Coyle, and G. Prasad, “Is Sensorimotor BCI Performance Influenced Differently by Mono, Stereo, or 3-D Auditory Feedback?” IEEE Transactions Neural Systems Rehabilitation Engineering, vol. 22, no. 3, pp. 431–40, May 2014.
[R5] D. Coyle et al., “Action Games, Motor Imagery, and Control Strategies: Toward a Multi-button Controller,” in Handbook of Digital Games and Entertainment Technologies, Singapore: Springer Singapore, 2015, pp. 1–34.
[R6] D. Coyle, J. Stow, K. McCreadie, J. McElligott, and Á. Carroll, “Sensorimotor modulation assessment and brain-computer interface training in disorders of consciousness,” Archives of Physical Medicine and Rehabilitation, vol. 96, no. 3, pp. S62–S70, 2015.
Indicators of research quality
[R1-R4, R6] have been peer reviewed by internationally-based editorial boards of the relevant journals. [R5] is peer reviewed handbook publication which contains novel results with spinal injured patients, co-authored with clinical collaborators and describes novel feedback technologies now in NeuroCONCISE products. Research and impacts are associated with
several international awards between 2008 and 2018 [C6]. Research in [R5] and [R6] was approved by multiple ethics committees and deployed and trialled with end-users – people with physical disabilities – and now in a national trial (NCT03827187). Prestigious grants have supported or followed from the research e.g.,
Damien Coyle, Intelligent Pre and Post Processing Algorithms for a Brain Computer Interface, EPSRC EP/H012958/1, 2009-2011, GBP101,096, to develop and trial BCI technology with physically impaired patients
Girijesh Prasad, Damien Coyle, Martin McGinnity, Innovations in Intelligent Assistive Robotics, UK-India Education and Research Initiative, 2008-2011, GBP144,557, to develop BCI for assistive robotics
Martin McGinnity, Damien Coyle, Liam Maguire, Girijesh Prasad, Computational Neuroscience Research Team, Department of Education and Learning Northern Ireland, 2008-2011, GBP1,535,807, to establish a Computational Neuroscience Research Team
Damien Coyle and Liam Maguire: Assessing and Optimising Human-Machine Symbiosis through Neural signals for Big Data Analytics, Defence Science Technology Laboratory, GBP215,535, 2014-2018, for cognition based neurotechnology R&D
Damien Coyle, NeuroCONCISE - Developing Brainwave Controlled Technologies, Invest Northern Ireland Proof of Concept Funding, 2013-2015, GBP106,000
Damien Coyle, Cognitive Based Computer Aided Engineering Design (CAED), EPSRC EP/M01214X/1, 2015-2019, approximately GBP1,300,000 with Strathclyde University (GBP147,142 to Ulster)
Damien Coyle and Karl McCreadie, The Spatial Computing and Neurotechnology Innovation Hub (SCANi-hub), Department for Economy & Ulster, 2019-2020, GBP266,277.
3 prestigious fellowships awarded to Prof Coyle
Assessing the Neural Correlates of Motor Learning and Control: Towards Adaptive BCIs, Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellowship, 2013-2014, GBP43,453
NeuroCONCISE project, Royal Academy of Engineering Enterprise Fellowship, 2015-2016, GBP60,000
UKRI Turing AI Acceleration Fellowship, AI for Intelligent Neurotechnology, EPSRC, (EP/V025724/1), 2021-2025, GBP1,807,387 (case for support underpinned by research and impacts described in this ICS).
4. Details of the impact
Following international award-winning research completed between 2003 and 2009 [R1 - R3, C6 - C6] trials with physically impaired patients (spinal cord injury, stroke and PDoC patients) began at the National Rehabilitation Hospital (NRH) of Ireland. PDoC patients are often incapable of reliable behavioural responses and thus behavioural based consciousness scales are insufficient to confirm awareness. We have collected substantial evidence (25 patients) that a subset of PDoC patients can modulate their brain activity to confirm consciousness and potentially enable interaction/communication without movement, thus impacting the patient, patient’s families and clinical teams. Associated impacts include:
I1: Establishing non-subjective evidence of awareness/consciousness in PDoC patients. For example, we used a BCI to assess awareness in a patient who had not communicated for 12 years, since suffering a brain injury as a teenager, and was considered to be in a minimally conscious state (MCS) . We provided non-subjective evidence of awareness and showed that the patient could modulate brain activity to interact with a computer in over twenty follow-up sessions between 2014 and 2018. The family reported immediate and long-lasting impact stating “[this] was a life-changing experience for our son… far better diagnosis of his condition… greater understanding of E’s capabilities… encouraged those working with E to redouble their efforts … reducing the amount of his medications [C4] ”. The research was top-10 nominated for the Annual BCI award 2015 [R6, C6]. Ethical approval for a national clinical trial with 17 hospitals across UK & Ireland has been granted (NCT03827187). Trials have been undertaken with twenty-five PDoC patients to date with additional reported immediate impacts as stated by clinical collaborators “… since 2014 the impact of the research has been deep and widespread, impacting patients, families, care teams, clinical interdisciplinary teams, awareness of spinal and brain injury, legal cases, international competitions and economically… from a clinical perspective the translational component of this research has been outstanding, enhancing and adding to real-time interdisciplinary team (IDT) therapeutic input, enhancing the clinical team-patient-family experience and incorporation of components of the BCI research in real-time patient care and augmenting IDT interventions.” [C7].
I2: Influencing the outcome of two medico-legal High Court cases. Research results with patient E and S have been presented in the High Court of Ireland (2015, 2020):- “the evidence that [Prof Coyle] gave to the High Court, in Dublin Ireland, in E’s case, against the Health Service Executive, Ireland, was instrumental in ensuring a successful outcome to the case which afforded E the financial ability to purchase the care regime [needed]…” (family, patient E) [R6, C4]; The High Court Judge’s report for patient E, [2015] IEHC 752, states the evidence by Damien Coyle “…was acknowledged … as substantially reflecting interaction with the plaintiff that was alternative to what was conducted [by others]” and “persuaded that some allowance should be made …in respect of Brain Computer Interface … annual sum of EUR3,750 (11-2015)… [and] EUR25,520 (11-2015) for provision of a Brain Computer Interface system…” [C4] . The family in the case of patient S stated “ Our legal team produced the report from Prof. Coyle and argued that this report clearly showed that S's brain function was coherent and stimulated when asked questions and that indeed she was aware of her surroundings and could make her preferred choices when given the option...this report had been a major contribution to getting the outcome we hoped to get... ” [R6, C5]. Solicitors in the case of patient S highlighting lasting impact in such cases stated that “…we have no doubt that [Prof Coye’s] services will be [the] cornerstone of the presentation of evidence in catastrophic injury cases…and … services will be required, used and approved by the Courts…” and the research report “…made a substantial financial difference to the level of damages we were in a position to claim and ultimately achieve for the client...” [C5]
I3: Enabling a spinal-cord injured (SCI) person to compete in Cybathlon. Between 2010 and 2011 EPSRC funded (EP/H012958/1) technology trials involving 11 people with spinal injury (3 reported in [R5]). Since 2016 one of the study participants competed with the Ulster team using our technology [R1 - R3] at the 1st Cybathlon (Zurich: ranking 6th), and subsequently in 2019 (Graz: ranking 3rd) and in 2020 (Global Format: ranking 6th). Enabling this person, who has had severe disability from SCI for over 20 years, to compete at an international level has impacted on the quality of life “…[the research] has had a major impact on my life and well-being…” [C8]. Cybathlon events have been attended by approximately 10,000 spectators, from “…over 100 countries…” and the organisers “…acknowledge the impact [Ulster] research and efforts have had on the establishment of the CYBATHLON initiative and raising awareness of disability globally...” [C9]
I4: Creating a start-up company that employed 5 people, selling software, hardware and services in Rwanda, UK, Bangladesh. In 2013 an Invest Northern Ireland Proof of Concept project produced a prototype neurotechnology product, wearable electronics and suite of software applications. This led to two prestigious Royal Academy of Engineering fellowships for Professor Coyle (including an Enterprise Fellowship). In 2016, NeuroCONCISE Ltd was founded and seed-funded by Innovation Ulster and TechStart venture capital funds, to commercialise AI-enabled, wearable technology. NeuroCONCISE provides a suite of products (hardware and software) and services and has sold products (valued at GBP49,500) in 3 countries, has raised approximately GBP350,000 grant income (Innovate UK Project Nos. 103607, 104959), GBP210,000 equity investment, employed 5 FTE (83 person months) and has engaged multiple subcontractors across the UK [C10.1-3], won multiple awards including the IET Innovation Awards Best Start-up category 2018 and the inaugural IET & E&T Innovation of the Year Award 2018 where the new top award was chosen by the E&T editorial team, who said: "The judges felt that this project was part of something bigger, something genuinely important. It pulls on many interesting disciplines such as AI, electronics, software and big data, and interweaves them in a solution that promises real developments in the field. In terms of ‘engineering a better world’ this ticks all the boxes." [C6], [R1 - R6].
I5: Contributing to national reports that led to the formation of UK KTN Neurotechnology Innovation Network and UK Neurotechnology roadmap. On the basis of EPSRC (EP/H012958/1) funded research [R1 - R3] Prof Coyle was invited to contribute to a parliamentary report on assistive technologies [C1] and provide expert advice to Nuffield Council on Bioethics who reported on Novel Neurotechnologies in 2013 [C2]. Both reports have been cited in a proposal by KTN that led to the successful establishment of the Knowledge Transfer Network Neurotechnology Innovation network (March 2019) (where Professor Coyle is now an advisory board member) as acknowledged by KTN. “Your research, the neurotechnology research at the Intelligent Systems Research Centre and recently established Spatial Computing and Neurotechnology Innovation Hub along with your spinout company, NeuroCONCISE Ltd, has substantially impacted on [...] neurotechnology awareness in the UK, providing underpinning evidence that UK has a burgeoning and growing neurotechnology sector and driving the [KTN Neurotech] initiative… The impactful research you presented at a number of our workshops… [3 events] … has helped inform a much wider audience about the potential impact of neurotechnology, not only for the health sector, but also non-medical applications … Prof Damien Coyle helped design …a survey …with … response from across the neurotechnology community and has helped shape …a transformative roadmap for UK neurotechnology... and… also fed into the Regulatory Horizons Council’s recent study on regulatory reform around medical devices” [C3].
5. Sources to corroborate the impact
C1: Parliamentary report on Research and Development Work Relating to Assistive Technology 2011 to 2012 (cited in testimony C3).
C2: Nuffield Council on Bioethics report on Novel Neurotechnology: Intervening in the Brain, 2013. A family involved in Ulster trials is quoted (5.2, p.107) (cited in testimony C3).
C3: Testimony from the KTN Knowledge Transfer Manager Emerging Technologies who led the establishment of the KTN Neurotechnology Innovation.
C4: Testimony from family of patient E; High Court Report [2015] IEHC 752 for patient E.
C5: Testimony from family of patient S; testimony from legal team of patient S following successful High Court case.
C6: Awards certificates: International BCI Research award competition 2015 Top 10 nomination certificate; IEEE Computational Intellig. Soc. Outstanding Doctoral Dissertation Award, 2008; International Neural Network Society Young Investigator of the Year Award, 2011; IET Innovation awards best start-up category winner 2018; IET and E&T Innovation of the Year Award 2018.
C7: Testimony from clinical partners at the National Rehabilitation Hospital.
C8: Testimony of patient O who competed at the Cybathlon competition 2016, 2019 and 2020.
C9: Testimony from organisers of the Cybathlon 2016, 2019 and 2020.
C10: NeuroCONCISE company; NeuroCONCISE investment profile; NeuroCONCISE Overview and Product brochure; NeuroCONCISE Management accounts to 31 Dec 2020.
- Submitting institution
- University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Summary impact type
- Health
- Is this case study continued from a case study submitted in 2014?
- No
1. Summary of the impact
Ulster has impacted epilepsy and stroke patients, across the island of Ireland, via the Northern Ireland Functional Brain Mapping Facility. Impacts include:
l1 – Epilepsy patients have been scanned and presurgical evaluation reports informed successful life-changing operations involving surgical brain resection at Beaumont Hospital, Dublin, Ireland.
I2 – Change of clinical practice for presurgical evaluation of patients with refractory epilepsy in Northern Ireland and the Republic of Ireland (taking an all-island approach to Epilepsy Care and Treatment).
l3 – Chronic post-stroke patients have achieved significant upper limb motor function recovery following brain-computer interface-driven hand exoskeleton rehab therapy over multiple sessions.
2. Underpinning research
Since 2002 Ulster’s Intelligent Systems Research Centre (ISRC) (formerly Intelligent Systems Engineering Lab) has been undertaking research in neuroscience and neurophysiology and building sustained expertise and infrastructure for brain research, beginning with a brain-computer interface (BCI) lab with electroencephalography (EEG) equipment (GBP140,000) in 2004, then establishing a computational neuroscience research team (GBP1,540,000) in 2009, followed by the Northern Ireland Functional Brain Mapping (NIFBM) Facility (GBP5,300,000) in 2014 and, more recently, the Spatial Computing and Neurotechnology Innovation Hub
(GBP360,000), building a strong track-record in computational and cognitive neuroscience, neuroimaging and neurotechnology R&D. The case for investment in these facilities within a Computer Science and Informatics Unit was strongly evidenced by the interdisciplinary underpinning research across a range of neuroimaging modalities – EEG, magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and functional near infrared spectroscopy (fNIRS) by ISRC researchers.
Developing this underpinning research and infrastructure, coupled with the associated competency and expertise, has enabled research that has had lasting impact on patients with stroke, and life-changing impact on patients with epilepsy – and has given medical practitioners confidence to engage the NIFBM as a facility for the assessment of patients and one which can inform patient care and treatment as evidenced in [C1-C6]. Significant research advances that underpins the above impacts, clustered in two main topics and mapped to references [ R1-R6] and the specific impacts ( I1-I3) are:
R1-R3 – Presurgical evaluation of epilepsy patients [l1, I2]: Magnetic source imaging (MSI), combining MEG and structural cerebral MRI, is increasingly being used for presurgical evaluation of patients with focal epilepsy, particularly when: (a) there are undetermined, or multiple epileptic foci; and (b) there are no identifiable lesions. Ulster and UPMC Paris’s
collaborative research led by Prof. Coyle in 2009-10 [R1, R2], which presents novel methods for (f)MRI structural/functional analysis, has enabled advanced analysis of patient MRI scans supplied by clinical collaborators prior to patients having MEG scans at the NIFBM. Furthermore, a highly advanced method for estimating brain functional connectivity has been developed by NIFBM researchers led by Prof. Prasad [R3] during 2015-17. Thus, the team has developed methodology and acquired advanced level expertise necessary for MEG-based presurgical evaluation of epilepsy patients.
R4-R6 – Feasibility trials with stroke patients [l3]
Mental practice (MP) in conjunction with physical practice of goal-directed rehabilitation tasks, enhances functional recovery of paralyzed limbs among stroke sufferers. BCI supported motor imagery practice with (gamified) visual feedback can support this type of rehab therapy. Ulster researchers led by Prof. Prasad [R4] reported the first such results from a pilot trial conducted in 2007-08, involving five people with chronic stroke, showing clinically important effects on upper limb function (patent submitted: 0821877.8). This was a precursor to [I3] and the Ulster and
Indian Institute of Technology Kanpur (IITK) collaborative research led by Prof. Prasad in 2014-17 [R5, R6], which transitioned from using a visual feedback of sensorimotor response to physical feedback through a hand exoskeleton actuated by the BCI response (Indian patent: 84996) and, subsequently, understanding the influence of the rehab therapy on brain function through combined EEG and MEG (supported by three (GBP144,557, GBP45,200 and GBP144,546 UK-India Education and Research Initiative (UKIERI) funded projects in collaboration with IITK, 2008-2020). The most recent findings demonstrate a significant and lasting effect of the therapy.
3. References to the research
Outputs can be provided by Ulster University on request.
R1 – X. Li, D. Coyle, L. Maguire, T. M. McGinnity, D. R. Watson, and H. Benali, “A least angle regression method for fMRI activation detection in phase-encoded experimental designs,” Neuroimage, vol. 52, no. 4, pp. 1390–1400, 2010.
R2 – X. Li, D. Coyle, L. Maguire, D. R. Watson, and T. M. McGinnity, “Gray matter concentration and effective connectivity changes in Alzheimer’s disease: A longitudinal structural MRI study,” Neuroradiology, vol. 53, no. 10, pp. 733–748, 2011.
R3 – J. M. Sanchez Bornot, K. F. Wong-Lin, A. L. Ahmad, and G. Prasad, “Robust EEG/MEG Based Functional Connectivity with the Envelope of the Imaginary Coherence: Sensor Space Analysis,” Brain Topography, vol. 31, no. 6, pp. 895–916, 2018.
R4 – G. Prasad, P. Herman, D. Coyle, S. McDonough, and J. Crosbie, “Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study,” BMC Journal of NeuroEngineering and Rehabilitation, 7(60), 1-17, 2010.
R5 – Chowdhury, A., Meena, Y. K., Haider, R., Bhushan, B., Uttam, A. K., Pandey, N.,Prasad, G. (2018). Active physical practice followed by mental practice using BCI-driven hand exoskeleton: a pilot trial for clinical effectiveness and usability. IEEE journal of biomedical and health informatics, 22(6), 1786-1795.
R6 – Rathee, D., Chowdhury, A., Meena, Y., Dutta, A., McDonough, S., & Prasad, G. (2019). Brain-Machine Interface Driven Post-Stroke Upper-limb Functional Recovery Correlates with Beta-band Mediated Cortical Networks . IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), 1020-1031.
Comment on Research Publications: All outputs have been peer reviewed by the internationally-based editorial boards of the journals. Research in [ R1-R2] was undertaken as part of the establishment of a Computational Neuroscience Research Team funded by the Department of Learning as a Cross Border Initiative in collaboration with Trinity College Dublin (GBP1,540,000) and involved other international collaborators (Benali). Research in [ R3 – R6]
were performed under the NIFBM facility development funding (GBP5,300,000) as well as international collaborations through three (GBP144,457, GBP45,200 and GBP144,546) UK-India Education and Research Initiative (UKIERI)-funded projects in collaboration with IIT Kanpur, India between 2008 and 2020.
- McGinnity, Coyle, Prasad and Maguire.
Computational Neuroscience Research Team.
All-island Cross-Border R&D Programme.
03/11/2008 - 31/03/2011. GBP1,535,807.
- Prasad, Bjourson, Cecotti, Wong-Lin, Coyle, Maguire, Coleman and McGinnity.
NI Functional Brain Mapping Facility. Invest NI, Grant for R&D – Capital Project.
01/04/2013 - 15/03/2020. GBP2,607,301. (Total Value ~GBP5,300,000 - 50% provided by Invest NI).
- Coyle and McCreadie.
The Spatial Computing and Neurotechnology Innovation Hub (SCANi-hub).
Department for Economy and Ulster University, Higher Education Capital Funding.
02/01/2019 - 31/12/2020. GBP266,227.
- Prasad, Coyle and McGinnity.
Innovations in Intelligent Assistive Robotics.
UK-India Education and Research Initiative (UKIERI), DST - UKIERI call 2007.
01/01/2008 - 31/12/2011. GBP144,557.
- Prasad, McDonough and Dutta.
A BCI Operated Hand Exoskeleton-based Neurorehabilitation System for Movement Restoration in Paralysis.
UK-India Education and Research Initiative (UKIERI), DST - UKIERI call 2013.
15/04/2014 - 31/05/2017. GBP45,200.
- Prasad, McDonough and Dutta.
Advancing MEG-based Brain-Computer Interface Supported Upper Limb Post-Stroke
Rehabilitation.
UK-India Education and Research Initiative (UKIERI), DST - UKIERI call 2016.
01/04/2017 - 31/12/2020. GBP144,546.
4. Details of the impact
The NIFBM facility houses the only magnetoencephalography (MEG) laboratory on the island of Ireland (and, to our knowledge, the only one housed in a computer science research facility in the UK, perhaps the world). NIFBM has a focus on clinical research and applied clinical applications – positioning the facility uniquely to facilitate neuroimaging, brain dynamics analysis, interdisciplinary knowledge and expertise that inform life-changing operations and interventions. Impacts include:
I1: Presurgical evaluation of epilepsy patients using advanced Magnetic Source Imaging
Refractory epilepsy is characterized by frequent recurrent seizures that are resistant to medication. A systematic review and meta-analysis (2015) found that surgery offers patients with refractory epilepsy an improved quality of life and a better chance of becoming seizure-free. In January 2015 Consultant Neurologist/Neurophysiologist at Beaumont Hospital Dublin, Ireland,
and Clinical Lead of the National Epilepsy Programme in Ireland [C1], contacted the NIFBM facility to discuss the clinical application of MEG for pre-surgical evaluation of refractory epilepsy patients, particularly when there is an undetermined location of seizure-onset.
To date, the NIFBM facility, underpinned by methods and knowledge presented in [R1-R3], has completed a presurgical evaluation for seven patients under the care of the Consultant Neurologist, 3 of whom are detailed in [C1, C3] “ …MEG* has a well-defined role in the identification of epileptiform dipole, deep to the sensitivity of surface EEG and immune to the characteristics of skull breach, highlighted well in [patient A’s] case…” and “…MEG* provided critical understanding of the location of the seizure onset zone within the large region of dysplasia. In addition motor evoked potentials, identified the primary motor regions, which were adjacent, but distinct from the seizure onset zone. In this case MEG served a critical role in operative planning, and [patient R] underwent a focal palliative resection, dissecting out the bulk of the peri-sylvian dysplasia, with particular attention to the epileptiform region as highlighted by MEG study and sparing similarly recognised Motor Regions within the MRI imaging abnormality. He is now employed, leading an active professional and social life, and has been free of seizure with associated alteration of awareness and generalised convulsion since 2016”. and “MEG study identified independent bi-hemispheric epileptiform foci… and provided accurate informative information with regard to the currently active source for seizures in [patient R’s]
case, and we hope to incorporate repeat study when future therapeutic options are available”.
All reports added clinical value, aiding in decision-making processes that led to improved outcomes for all patients.
* (in the letter meaning our MEG imaging and analysis)
I2: Change of practice for presurgical planning of epilepsy patients on the island of Ireland using clinical MEG
The Clinical Lead of the National Epilepsy Programme in the Republic of Ireland [ C1] has made progress in joining forces with colleagues in Belfast to establish an all-island Epilepsy Surgical Programme for adults and paediatrics. This has been promoted and supported by a Consultant Radiologist, who has worked with the Clinical Lead of the National Epilepsy Programme and the NIFBM to establish a clinical programme via the NIFBM. The Consultant Radiologist was instrumental in promoting the use of NIFBM for epilepsy care in Ireland stating
[C2] “… Arising from the development of NIFBM and the successful presurgical evaluation of epilepsy patients going for life changing surgery for epilepsy treatment in 2018 we have made significant progress towards establishing clinically applied MEG services in Ireland – specifically the launching of an all-Ireland epilepsy programme, and the progression of MEG as diagnostic tool for traumatic brain injury (TBI)...”. Following the success of the impacts highlighted in [I1] it has been recommended by the Clinical Lead of the National Epilepsy Programme that clinical care for patients with refractory epilepsy will involve a presurgical evaluation using MEG, given the added clinical value MEG brings to the presurgical evaluation procedure “… repeated study and incorporation in our clinical decision process, will allow for the further development of MEG at NIFBM, and has the potential to lead patients with otherwise life limiting Epilepsy towards the prospect of seizure freedom...” The NIFBM facility, as the only MEG in Ireland and given the expertise available at the facility [R1-R3], will be engaged to undertake these evaluations when the facility is fully established for clinical care. Currently approximately 60,000 people across the Rep. of Ireland and Northern Ireland suffer from epilepsy. Refractory epilepsy (resistant to medication) can occur in 20% to 40% of cases, having a debilitating impact on the patient – loss of self-confidence, livelihood, adverse effects on education and relationships, increased risk of memory deficits (particularly long-term, which includes autobiographical memory), and increased risk of depression and anxiety disorders. Thus, the change in patient care stimulated by the impacts [I1] not only frees more patients from recurring and frequent seizures, but significantly improves patients’ quality of life and their clinical outcomes relating to these secondary impacts as evidenced on [C1, C2].
I3: Brain-computer Interface supported post-stroke neurorehabilitation
A novel BCI-driven hand exoskeleton supported rehab therapy has undergone phase-2 trials
[R5]. Pilot clinical trials on post-stroke participants have been conducted in two phases. The first phase involved ten healthy individuals and ten chronic stroke patients participating in a feasibility study followed by the participation of four chronic stroke patients in a longitudinal pilot trial in India during Aug-Sept 2016. In the 2nd phase during Apr-May 2017 at Ulster, five post-stroke patients undertook up to twelve sessions of rehab therapy and underwent weekly resting state MEG scanning to assess recovery related cortical changes [R6].
A standard hand functional recovery measurement, the action-research arm test (ARAT), revealed that all the chronic stroke participants that underwent ten or more therapy sessions in the clinical trial achieved clinically important improvement in their hand function [R5, R6] and all reported transformative change in their quality of life because of the recovery of upper limb functional use and in improvement of overall mental health. A testimony from a patient as a video interview as well as a handwritten letter [C4] highlights multiple impacts this improvement in function has had e.g., “…more coordination of my hand picking things up… like a button… I can touch back of my head… hold my umbrella steady… tie my shoe laces… I feel more confident when I go out… to restaurant, using knife + fork... [looking forward] to upcoming wedding”, which were earlier not possible. A report from the occupational therapist details the improvement in patient outcomes following engagement with the technology developed at the NIFBM facility, “…having undergone the pilot trials the stroke participants have reported transformative change in their quality of life, particularly in the recovery of upper hand motor functions. This is clearly evident from the video interview I conducted with at least one of the patients” [C5, C6].
5. Sources to corroborate the impact
C1 – Letter from a Consultant Neurologist/Neurophysiologist at Beaumont Hospital Dublin, Rep. of Ireland.
C2 – Letter from a Consultant Radiologist at Health Service Executive, Rep. of Ireland.
C3 – Presurgical reports presented for 3 patients.
C4 – Testimonials from a stroke patient.
C5 – Letter from a Senior Occupational Therapist, Altnagelvin Hospital, Derry-Londonderry, who helped in clinical trials, detailing improvements observed in stroke patients.
C6 – A video interview with a stroke patient.
- Submitting institution
- University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Summary impact type
- Societal
- Is this case study continued from a case study submitted in 2014?
- No
1. Summary of the impact
The impacts described in this case study include changes in policy and practice across
several organisations as a result of the insights produced by novel application of machine learning to their data, including:
I1 – Improved caller management for major national helplines from crisis helpline analysis
research;
I2 – Major digitalisation of assistance programmes for employees and vulnerable groups from mental health chatbot research; and
I3 – National NHS reminiscence app from reminiscence research (the recollection of past events particularly important to support the memory of people with dementia) for people with dementia and their carers.
2. Underpinning research
Since 1996, Ulster University has led in the development of state-of-the-art algorithms in machine learning and data analytics, including association rule mining and other algorithms licensed to SPSS and IBM. In the current research period, Professor Maurice Mulvenna and Dr Raymond Bond led in collaborating with colleagues at Ulster in Psychology and in Nursing, and the resulting underpinning research has been strongly interdisciplinary in nature, including 3 strands of research with 3 accompanying research impacts [I1-I3]:
Archetypal caller types identified based on interactional behaviour. National crisis helpline data from Samaritans UK (2017, 21.9M calls), Samaritans Ireland (2016, 3.5M calls) and Lifeline NI (2017, 637K calls) were analysed using machine learning algorithms. Research demonstrated that archetypal caller types were identifiable based on interactional behaviour. Clustering was conducted and five main caller archetypes identified in UK data also matched the five call archetypes identified in the Ireland data set, confirming the same characteristic caller archetypes across two national territories [R1]. Caller archetype prediction research identified rule sets generated from C5.0 decision tree classifier algorithm classification could be used to predict caller archetype [R2].
Our research with Inspired Wellbeing (2015-2020) developed artificial intelligence-based chatbot research and delivered a new Inspire Support Hub that provided a chatbot based on the Microsoft Bot Framework with conversational access to a range of information, guidance, screening and intervention tools, tailored specifically to care for employees’ wellbeing needs [R3]. Our research won the Societal Impact award at the National KTP Awards 2020; and
Increase in mutuality, quality of caregiving relationships, and emotional well‐being for people living with dementia. Ulster researchers have long been active in researching reminiscing, chairing the British HCI Workshop on Reminiscence Systems, in Cambridge, UK in 2009 and the CHI Workshop on Bridging Practices, Theories, and Technologies to Support Reminiscence, in Vancouver, Canada in 2011. Subsequently, at Ulster University, in 2014, a quasi-experimental feasibility study investigated the outcomes of a home based, individual specific reminiscence intervention using an iPad app called InspireD for people living with dementia and their family carers. The work was funded by Northern Ireland’s Public Health Agency (PHA) and completed in 2018. The increase in mutuality, quality of caregiving relationships, and emotional well‐being for people living with dementia scientifically demonstrated for the first time the value of reminiscing [R4]. Our research confirmed that a more individualised relationship-centred approach to reminiscence, facilitated through the use of the InspireD app [R5], generates a positive effect on people living with dementia without negative consequences for family caregivers. These findings, confirmed by our ecological momentary assessment research [R6], support emerging global evidence that suggests individual specific psychosocial interventions are effective in dementia care.
3. References to the research
[R1] O'Neill, S., Bond, R.B., Grigorash, A., Ramsey, C., Armour, C., Mulvenna, M.D., (2018) Data analytics of call log data to identify caller behaviour patterns from a mental health and wellbeing helpline, Health Informatics Journal, 25(4): 1722-1738,
https://doi.org/10.1177/1460458218792668.
[R2] Grigorash, A., O'Neill, S., Bond, R.R., Ramsey, C., Armour, C., Mulvenna, M.D., (2018) Predicting Caller Type Using Call Log Data from a Mental Health and Wellbeing Helpline, JMIR Mental Health, 5(2): e47. https://doi.org/10.2196/mental.9946.
[R3] Cameron, G., Cameron, D., Megaw, G., Bond, RR., Mulvenna, M., O'Neill, S., Armour, C., & McTear, M. (2019). Assessing the Usability of a Chatbot for Mental Health Care. In: A. Følstad, H. Halpin, H. Niedermayer, S. S. Bodrunova, A. Smoliarova, O. Koltsova, P. Kolozaridi, & L. Yuldashev (Eds.), Internet Science: INSCI 2018 International Workshops, St. Petersburg, Russia, October 24–26, 2018, Revised Selected Papers (Vol. 11551, pp. 121-132). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11551 LNCS). Springer Verlag.
https://link.springer.com/chapter/10.1007/978-3-030-17705-8_11.
[R4] Laird, E.A., Ryan, A.A., McCauley, C.O., Bond, R.B., Mulvenna, M.D., Curran, K., Bunting, B.P., Ferry, Finola, Gibson, A. (2018) Using mobile technology to provide personalised reminiscence for people living with dementia and their carers: Appraisal of outcomes from a quasi-experimental study, JMIR Mental Health, 5(3): e57, https://doi.org/10.2196/mental.9684.
[R5] McCauley, C.O., Bond, R.B., Ryan, A.A., Mulvenna, M.D., Laird, E.A., Gibson, A, Bunting, B.P., Ferry, F., Curran, K. (2019) Evaluating User Engagement with a Reminiscence App Using Cross-Comparative Analysis of User Event Logs and Qualitative Data, Cyberpsychology,
Behavior, and Social Networking, 22(8): 543-551. https://doi.org/10.1089/cyber.2019.0076.
[R6] Potts, C., Bond, R., Ryan, A., Mulvenna, M.D., McCauley, C., Laird, L., Goode, D., (2020) Ecological Momentary Assessment Within a Digital Health Intervention for Reminiscence in Persons With Dementia and Caregivers: User Engagement Study, JMIR mHealth and uHealth, 8(7): e17120, https://doi.org/10.2196/17120
Indicators of research quality
All journal articles have been subject to double-blind peer review practice by internationally based editorial boards and review teams. [R3] is associated with awards: Winner, National KTP Best of the Best Awards 2020, Societal Impact Award; Winner, Inaugural Award for Industry-Academic Collaboration, Centre for Behaviour Change Conference on Behaviour Change for Health: Digital and Other Innovative Methods; and Outstanding grade for KTP with Inspire Wellbeing Ltd, 2018. The following grants supported or followed from the research:
Prof A Ryan, Prof M Mulvenna, Dr C McCauley, Dr L Laird, Dr D Goode, Dr R Bond, “Developing and testing the InspireD reminiscence app”, HSC R&D DARUG, 2019-2023, GBP85,883.
Prof S O'Neill, Dr S Armour, Dr R Bond, Prof M Mulvenna, Ms C Potts, Mr R Turkington, “Data analytics and dashboard development for Samaritans helplines”, Samaritans Ireland, 2016-2020, GBP62,062.
Prof M Mulvenna, Dr R Bond, Prof S O'Neill, Dr C Armour, “Mental health chatbot and platform for employee assistance programme” ESRC Knowledge Transfer Programme with Inspire Wellbeing (formerly Carecall (NI) Ltd), 2016-19, graded as ‘Outstanding’ KTP partnership & Winner of CBC Inaugural Award for Industry-Academic Collaboration, 2019, GBP100,300.
Prof A Ryan, Dr L Laird, Prof K Curran, Prof B Bunting, Prof M Mulvenna, Dr F Ferry, Dr R Bond, “A feasibility study of facilitated reminiscence for people with dementia”, HSC R&D, 2015-2019, GBP320,364.
4. Details of the impact
I1. Improved caller access to helplines: In our research [R1-R2], five cluster archetypes were identified, for example, high frequency callers making thousands of calls over time. Helpline Manager, Samaritans Ireland, said “ *The identified cluster archetypes and toolsets developed have become part of the lingua franca for Samaritans, used in analysing awareness campaigns and most recently, examining the impact of COVID-19 on the helpline. The research has now been replicated across the UK as well and the initial tools are being developed into a dashboard.*” [C1]
Policy Officer, Samaritans Ireland, said “ The data analytics approach by Ulster has provided us with solid quantifiable metrics of the impact of government policies and other events on public mental health through our helpline. We are now applying these metrics to serve operationally as real time mental health monitors of public levels of wellbeing.” [C1]
Since April 2020, research on caller archetypes and queuing was operationalised in a live dashboard service for the Samaritans, helping them better understand callers in real-time and improve service provision. In using queuing to manage high frequency callers and move their behaviour to that of regular callers, this freed up around 50% of the time previously taken to answer and speak with these callers, enabling Samaritans volunteers to answer significantly more calls. [C1]
I2. New assistance programmes for employees and vulnerable groups: Our collaborative research with Inspire Wellbeing [R3] co-developed a new digital mental health service called the Inspire Support Hub as part of our award-winning Knowledge Transfer Partnership. [C2, C3, C4, C5]
Inspire Chief Executive Officer said, “ *If we did not have the foresight, bolstered by the Ulster University team’s belief in the direction to take, then our company would now be playing catch up in digital employee access programs and student services.*” [C2]
In terms of impact, Inspire Director of Professional Services said, “Since June 2019, our Inspire Support Hub is used by over 350 clients across a range of sectors. This includes 560,000 employees in the workplace across Ireland, over 180,000 students in further and higher education across Ireland and over 60,000 clients in specialist occupational groups such as the emergency services and our local veteran community”. [C2]
An Inspire service user said, “ The Inspire Support Hub has really helped me get through the past few months. It is easy to use, with lots of information and I have been able to log in any time. I have been using the Hub to help me manage my drinking which has really worked during the lockdowns. I would have struggled without the help from Inspire.” [C2]
I3. NHS digital reminiscence app: Our work [R4-R6] which presents and assesses an app provided direct evidence to the NHS on the viability of apps for people with dementia, in terms of uptake, sustained engagement, and on the value of event logging to help understand anonymous app usage, leading to the Ulster-coordinated Apple and Google InspireD app development and rollout in 2020. [C6, C7, C8, C9]
Feedback from a person living with dementia said of the app, “ *I put everything to do with my life and the people I love inside a little piece of machinery that is wonderful. At the touch of a button, it can reflect everything that has happened to me in my past and the lovely people I’ve met.*” [C6]
‘The Songbirds’ play, commissioned by the InspireD team and based on the findings of the research, described as “ insightful and educational” by the Alzheimer’s Society, had audiences of approximately 2,000 across 14 showings. A person living with dementia described the play as “ just like watching myself. …. it gives a 100% perfect representation of a person with dementia, that is the truth”. [C6]
The Innovation & Digital Eco-system lead in Health and Social Care, Department of Health in Northern Ireland said: “ Working collaboratively with Ulster University, the InspireD research and app is part of a wider digital transformation programme in health and social care which is being led by Digital Health & Care NI (DHCNI) with the support of DoH. Using a co-production approach DHCNI works across the health and social care organisations, governmental bodies, academic, industry partners and community & voluntary sector representing patients and carers to develop our digital eco-system. Our partnership with Ulster University on the InspireD App-research, development and roll-out is an example of this new programme of engagement. The overall aim is to embed clinically assured digital solutions that have been checked for data privacy and security into our clinical pathways.” [C7, C9]
5. Sources to corroborate the impact
C1. Correspondence from Samaritans on impact on policy and practice.
C2. Testimonial from Inspire Wellbeing on impact of Inspire Support Hub.
C3. ‘Outstanding’ grade certificate from Innovate UK for Knowledge Transfer Partnership.
C4. ESRC Blog posting on ESRC site.
C5. CMSWire Press story (“7 Examples of Digital Workplace Chatbots”, 8 February 2019).
C6. “A Feasibility Study of Facilitated Reminiscence for People Living with Dementia”. Report to HSC and Correspondence from Dementia NI.
C7. Testimonial from Public Health Agency on Dementia Apps Library and InspireD reminiscence app commissioning.
C8. New online service for those living with dementia, HSC website.
C9. Inspired app (Google Play/Apple App).
- Submitting institution
- University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Summary impact type
- Societal
- Is this case study continued from a case study submitted in 2014?
- No
1. Summary of the impact
Ulster’s research in reminding solutions has contributed novel findings in medication management workflow, mobile-based video reminders for people with dementia and online training platforms for caregivers. This research underpins the following impacts:
I1 – Internet based product for managing the logging and dispensing of medication leading to 8 jobs, uptake by 150 users across 5 care homes and annual revenue of GBP1,200,000.
I2 – Online training platform for carers of people with dementia with 80 individual licences and 13 group sales in 6 EU countries.
I3 – Establishment of new policy guidelines for 45,000 Social Care Council staff in Northern Ireland and impacting the life and health sciences sector in Northern Ireland.
2. Underpinning research
The increase in the number of people suffering from long term health conditions coupled with the rapid decline in the working population has led to predictions of a decrease in the number of healthcare professionals who will be available to offer healthcare provision in the future. Taking this into consideration, new models of care delivery are required, in addition to the up-skilling of untrained informal caregivers and volunteers who will be expected to deliver healthcare, in order to ensure that a continuum of care is provided to those in need.
Research led by Nugent within the Pervasive Computing Research Centre (PCRC) has had a focus on developing solutions to support stakeholders within a Connected Health environment for over 20 years. The research and main application areas have related to reminding technologies for medication management and supporting people with dementia and their caregivers which are linked to research R1-R6, linked to impacts I1-I3 and evidenced in [C1-C7].
R1-R2 Reminding technologies for medication management
The initial work in this area stemmed from the EU FP5 MEDICATE Project (2000-2004), where Ulster were Project co-ordinators. The MEDICATE Project assessed stakeholder needs in the process of reminding technologies and established an Internet-based care model to support all those involved in the supply-to-intake chain of personal medication management [ R1]. The MEDICATE solution was subsequently extended to a mobile-based reminding application which inspired the concept of video-based reminders for persons with dementia [ R2]. Through over 400 days of evaluations, the results from this research demonstrated the utility of the solution from both caregiver and patient perspectives. Two key areas were identified for future work: improving education for stakeholders using the technology, and challenges with technology adoption.
R3-R4 - Training for both formal and informal caregivers of persons with dementia
Involvement in the EU LLP STAR Project (2010-2014) permitted the PCRC to investigate the most appropriate digital strategies to support training for both formal and informal caregivers of persons with dementia (Nugent and Paggetti). A multilingual e-learning tool was designed and developed, taking into consideration varying levels of training content, gamification and user interaction strategies for engagement. The portal was evaluated in a randomized controlled trial with both informal caregivers and professional caregivers (n=142) in the Netherlands and the UK over a period of 4 months. The results demonstrated a significant positive impact of the STAR training course on maintaining feelings of empathy among informal caregivers and volunteers. It was also found that for professional caregivers, there was an improvement in empathy amongst those who followed the course. In terms of usefulness and user friendliness, the tool was rated positively by all user groups [ R3].
Research within the EU MSCA REMIND Project (2017-2020), for which the PCRC are the project co-ordinators (Nugent and Cleland), has permitted work to continue on how reminding technologies can be embedded within smart environments. This research has led to training initiatives for caregivers [ R4] (Synnott and Nugent) in addition to the delivery of 4 annual summer schools for early career researchers [ C4] (Nugent, Paggetti and Cleland).
R5-R6 – Technology adoption models for persons with dementia
To assist with engagement strategies and technology adoption, research was undertaken to develop a suite of technology adoption models [ R5] (Nugent and Cleland). Such models have been developed to predict the likelihood of a person with dementia adopting the technology. This work has been further extended to produce a set of recommendations to assist in the update of technology-based solutions both for persons with dementia and their caregivers [ R6].
3. References to the research
[ R1] CD Nugent, D Finlay, RJ Davies, MD Mulvenna, JG Wallace, C Paggetti, E Tamburini, ND Black (2007). The Next Generation of Mobile Medication Management Solutions, International Journal of Electronic Healthcare, vol. 3, no. 1, pp. 7-31.
[ R2] MP Donnelly, CD Nugent, S Mason, SI McClean, BW Scotney, AP Passmore & D Craig (2010). A Mobile Multimedia Technology to Aid Those with Alzheimer's Disease. IEEE Multimedia, 17 (2), 42-51. doi: 10.1109/MMUL.2010.25
[ R3] B Hattink, F Meiland, H van der Roest, P Kevern, F Abiuso, J Bengtsson, A Giuliano, A Duca, J Sanders, F Basnett, C Nugent, Paul K, R-M Dröes (2015). Web-Based STAR E-Learning Course Increases Empathy and Understanding in Dementia Caregivers: Results from a Randomized Controlled Trial in the Netherlands and the United Kingdom, Journal of Medical Internet Research (http://www.jmir.org\), 30.10.2015.
[ R4] J Synnott, M Harkin, B Horgan, A McKeown, D Hamilton, D McAllister, C Trainor, CD Nugent, The Digital Skills, Experiences and Attitudes of the Northern Ireland Social Care Workforce towards Technology for Learning and Development: Survey Study, (2020) JMIR Medical Education, vol. 6, no. 2. doi: 10.2196/15936
[ R5] P Chaurasia, SI McClean, CD Nugent, I Cleland, S Zhang, M Donnelly, B Scotney, C Sanders, K Smith, M Norton, J Tschanz (2016). Modelling Assistive Technology Adoption for People with Dementia, IEEE Journal of Biomedical and Health Informatics, vol. 63, pp. 235-24.
[ R6] J. M. Robillard, I. Cleland, J. Hoey, CD Nugent (2018). Ethical adoption: A new imperative in the development of technology for dementia, Alzheimer’s and Dementia, vol. 14, no. 9, pp. 1104-113.
[ R1- R6] have been subject to peer review by internationally based editorial boards.
Grants:
• MEDICATE: The control, Identification and Delivery of Prescribed Medication, EU FP5 (IST-2000-27618) GBP417,267 (to Ulster), (Black, Nugent) Nov 2001 – Jan 2006
• STAR: Skills Training and Re-Skilling for Carers of People with Dementia, CEC - Leonardo da Vinci, GBP37,900, (Nugent, Liu, Donnelly, Wang) Dec 2010 - Feb 2014.
• TAUT: Technology Adoption and Prediction Tools for Everyday Technologies, Alzheimer's Association (ETAC-12-242841), GBP128,205 (to Ulster), (Nugent, Donnelly, Scotney, McClean) Nov 2012 - Oct 2015.
• REMIND: The use of computational techniques to improve compliance to reminders within smart environments, CEC-H2020-MSCA-RISE, GBP192,439. (Nugent, Charles, Cleland, Galway, Donnelly, McCullagh, Zhang, Morrow) Jan 2017 - Dec 2020.
4. Details of the impact
I1 Internet-based product for managing the logging and dispensing of medication leading to 8 jobs, uptake by 150 users across 5 care homes and annual revenue of GBP1,200,000. [ R1, R2, R5]
[text removed for publication], a pharmacy automation business based in [text removed for publication], Northern Ireland (NI), has taken the findings from our research and created a new internet-based product for the management of their medication provision in care home settings. In 2016 the company wished to expand its operations into the domiciliary care market, but did not have the experience nor the technology expertise to do so [ C1]. Through its engagement with the PCRC, the research findings and workflow management through an internet care model from the MEDICATE Project [ R1] and mobile medication solution [ R2] were used to create the design of a new mobile internet-based solution for tracking and managing medication sachets within care home settings. Findings from the digitisation of workflow management in the medication process [ R1, R2] in addition to approaches to improve technology adoption [ R5] were incorporated into the solution. [text removed for publication] subsequently leveraged funding to realise the product and in 2019 launched its new product offering, [text removed for publication]. This is now being used in 5 care homes across NI to support approximately 150 users. Since engaging with PCRC, [text removed for publication] has grown its team from [text removed for publication] to [text removed for publication] (representing an increase in headcount of [text removed for publication]). The company has since entered a new market of medication management in care homes and has increased its annual revenue from [text removed for publication] in 2016 to [text removed for publication] in 2020 [ C2]. Improvements have been found from both patient and healthcare professional perspectives as evidenced in documented testimonials [ C7]. The manager of Inspire Supported Living Care Home said that in using the automated processes of [text removed for publication] “ it is a simplistic system” which has “ drastically reduced our timeframe” for managing medication within the care home [ C7]. Families using the solution have advocated that it assists with managing complex medication regimes and that “ there is absolutely no confusion as to what that medication is”. Users of the solution who previously had been challenged with the complexity of their medication regime have reported that “ it has given me peace of mind” [ C7].
I2 Online training platform for carers of people with dementia with 80 individual licences and 13 group sales in 6 EU countries. [ R3]
Through involvement in the EU STAR Project and evaluation of the findings from the randomised control trial conducted by the consortium [ R3], PCRC joined with members of the consortium to develop a multi-lingual online training portal, the ‘STAR Training Portal’. Since 2015, the portal ( www.stratraining.eu) has generated 13 group sales and 80 individual licences in 6 EU countries UK, Netherlands, Romania, Italy, Malta and Sweden. The ambition was to deliver a low cost and affordable solution to support carers of people with dementia (25 Euros per user). Profits are split between partners within the original STAR consortium on a pro-rata basis aligned with the original Project funding and in accordance with a Joint Ownership and Collaboration Agreement [C3].
I3 Establishment of new policy guidelines for 45,000 Social Care Council staff in Northern Ireland and impacting the life and health sciences sector in Northern Ireland. [ R2, R4, R6]
Through extensive research on caring solutions and technology driven care models the PCRC has engaged with a number of agencies and initiatives to deliver impact across a broad range of care based training, policy development and advisory initiatives that have informed roadmaps for delivering connected health products and services for caregivers including:-
Northern Ireland Social Care Council (2018-2019)
Connected Health Summer School (CHSS) (2016-2019)
International Federation of Ageing
MATRIX Life and Health Sciences NI
The Health Innovation Research Alliance Northern Ireland (HIRANI)
The PCRC has worked with the Northern Ireland Social Care Council (2018-2019) to assist it in understanding and developing its digital capability for its 45,000 registered staff who deliver social work and social care services in Northern Ireland [ C5]. The development of the council’s digital skills for social care strategy has been based on the collection and analysis of staff feedback undertaken by PCRC [ R4][ C6]. This has resulted in an increase of 50% in users engaging with the council’s online learning zone for the promotion of technology within the workplace [ C5].
The PCRC has raised awareness of the use of technology for reminding purposes, to support persons with dementia and in general healthcare provision in a number of settings [ R2, R6]. Through the Connected Health Summer School (CHSS), which annually presents the research results from the REMIND consortium, the PCRC has co-organised and delivered the event over the last 4 years (2016-2019) to technologists, clinicians and industry practitioners [ C4]. In a post event assessment to determine the benefits of the CHSS, all respondents stated that the summer school would have an impact on their future careers; and, that improvements in knowledge across technical, health and business domains were achieved [ C4]. One industry participant stated the “ summer school has been an amazing experience. Both from a personal and from a professional development perspective. The direct contact with the problem owners offers a clear and full perspective of their needs and wishes. It also allows for direct, well-nuanced feedback of any solution direction that is being considered. The offer of both technical and health care oriented sessions show the full breadth of the potential solutions, allowing participants to look beyond their own discipline. Professionally, I have benefited greatly in my development. Hoping to contribute to the medical technology field, I am now organizing a boot camp in the Medical Device Regulation (MDR) for the second year in a row. In this boot camp, we bring medical technology companies, start-ups and students together to test specific use cases for compliance with the MDR.”
Based on a general appreciation for the expertise acquired by the PCRC from research in the area of technology-based solutions for care provision, Prof Nugent was invited to join a panel of technology experts by the International Federation of Ageing to produce the ‘Reablement and Older People’ guidelines for the design and implementation of technology driven care models [ C8].
Prof Nugent was also invited to join the MATRIX Northern Ireland Science Industry Panel, a business led expert panel, formed primarily to advise government, industry and academia on the commercial exploitation of R&D and science and technology in Northern Ireland. The Chair of the MATRIX Panel stated that Prof Nugent was appointed to the panel “based on his research and academic track record and that he was a valuable member of the panel and that his research helped to inform the content of the report and shaped the recommendations” [ C9].
The expert panel published the MATRIX Life and Health Sciences NI ‘Capability Assessment and Foresight Report’ in 2015. MATRIX commissions research, analysis and studies to build an evidence base for future science and R&D policies in Northern Ireland. One of its objectives is to ‘identify, agree and oversee a programme of market led science and technology Foresight studies’. The report outlined recommendations to government highlighting the need (amongst others) for the further training of healthcare professionals and in terms of clear leadership and co-ordination the report highlighted “the importance of building a committed, visible leadership and co-ordination capability for the sector. A facility whereby decision makers within industry, academia and government meet to make decisions for Northern Ireland would dramatically progress this issue” [C9] . Following recommendations from the report in 2019 Invest Northern Ireland have funded the establishment of The Health Innovation Research Alliance Northern Ireland (HIRANI) an alliance of universities, health organisations and other industry bodies, established to drive and support ambitious growth in Northern Ireland’s Life & Health Sciences sector. The Chair of the MATRIX Panel stated “The recommendations from the Matrix report absolutely led to the establishment of HIRANI as a vehicle to coordinate and lead the life and health sciences sector in NI. Moreover, the recommendations also formed the basis of Invest NI’s life science sector strategy informing areas of sectoral strength and approaches that might support business growth and expansion in the sector”. Research by the Fraser of Allander Institute in 2020 revealed that the life science sector in Northern Ireland is thriving, supporting 18,000 full-time jobs throughout Northern Ireland.
5. Sources to corroborate the impact
C1: Interview with Senior Pharmacist from [text removed for publication].
C2: Testimonial from [text removed for publication].
C3: Balance sheets for sales of STAR Training Portal.
C4: Connected Health Summer School Program assessment analysis.
C5: Testimonial from the Director of Registration and Corporate Services of the Northern Ireland Social Care Council.
C6: Technology for Learning and Development Survey, Northern Ireland Social Care Council
C7: Testimonials from users of [text removed for publication].
C8: International Federation of Ageing, Report on Reablement and Older People.
C9: MATRIX Report, Life and Health Sciences Panel, statement from Chair of MATRIX Panel.
- Submitting institution
- University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Summary impact type
- Technological
- Is this case study continued from a case study submitted in 2014?
- No
1. Summary of the impact
Ulster research on electrocardiography (ECG), which is used to detect cardiovascular disease (leading cause of death worldwide) resulted in the following impacts:
I1 – an ECG algorithm that has been used by the FDA [text removed for publication] to monitor the risk of drug-induced abnormal heart rhythms,
I2 – an interactive ECG recording simulator that was integrated into a medical textbook selling [text removed for publication], 2014-2019)
I3/I4 – research on automated cardiac defibrillation resulted in the [text removed for publication] and the approval of a new AED which has been used to save lives. Informing working standards/reports (IEC 80601-2-86, IEEE P7003).
2. Underpinning research
The research is described in 4 strands associated with research [R1-R6], linked to impacts [I1-I4] and evidenced in [C1-C10].
R1 – ECG algorithm for computing biomarkers The electrocardiogram (ECG) is a diagnostic tool for detecting cardiovascular disease (the most common cause of death worldwide). Ulster developed an algorithm in 2012 to transform the ECG into a 3D model to compute spatial ECG features (QRS-T angle) that are important for predicting mortality/morbidity. This work was in collaboration with the U.S. Food and Drug Administration (FDA) but was led by Ulster academics (Guldenring, Finlay, Bond). In collaboration with the FDA Ulster demonstrated that Ulster’s algorithm was more accurate than the state-of-the-art [R1]. This work was selected as an invited talk at the International Society for Computerized Electrocardiology. This algorithm has been adopted by the FDA and is used for computing biomarkers in drug trials. The FDA described and cited Ulster’s algorithm in a draft standard document and associated reports (IEC 80601-2-86).
R2-R4 – ECG recording errors and a computer simulator Many professionals are not aware of the effects of ECG recording errors and that electrode misplacement is a common error. Using ECG signals recorded from the entire torso, Bond et al. developed a simulator [R2] to allow users to see the effects of recording errors on signals and diagnoses [R3]. A paper describing the simulator was published in 2011 (Journal of Electrocardiography) but was also described alongside a detailed use case experiment in the Methods of Information in Medicine Journal [R2]. Research funded by the Higher Education Academy (Bond, Nugent, Finlay) demonstrated that the simulator improved the training of physiologists [C3]. This simulator was then integrated into a medical textbook (12th ed., Marriot’s Practical Electrocardiology). Ulster (Bond et al., 2012) used the simulator to research the effects of electrode misplacement [R3] and showed that there can be a 24% chance of a misdiagnosis. This research was identified by a company (eNNOVEA Medical) to progress the validation of their product (Cardio-Quick Patch) for reducing electrode misplacement errors. Ulster (Bond, Finlay)
worked with this company to validate this product showing that it reduces electrode
misplacement. This work also won best poster award in 2016 at the International Society for Computerized Electrocardiology.
R5 – User interface validation of a cardiac defibrillator HeartSine technologies (1997 Ulster spinout company) and Ulster carried out a study in 2016 to validate the efficacy of a novel automated defibrillator [R5]. Our study found that embedded computer programs in AEDs can provide automated audio-visual feedback that can assist users and improve their chest compression rates whilst maintaining a high chest compression fraction which is advocated by resuscitation guidelines. The work was published in IEEE Human Machine Systems Journal [R5].
R6 – Automation bias in ECG interpretation Ulster (Bond, Finlay) conducted studies in 2017/2018 to measure automation bias amongst doctors (when users naïvely over-trust algorithms). This was presented as an invited talk at the International Society for Computerised Electrocardiology and published in the International Journal of Medical Informatics and the Journal of Electrocardiology [R6]. Bond et al demonstrated that the doctor’s accuracy in reading ECGs can drop from 86.38% to 27.43% when the algorithm provides unreliable advice.
3. References to the research
[R1] Guldenring D, Finlay DD, Bond RR, Kennedy A, McLaughlin J, Galeotti L, Strauss DG. The derivation of the spatial QRS-T angle and the spatial ventricular gradient using the Mason–Likar 12-lead electrocardiogram. Journal of Electrocardiology. 2015 Nov 1;48(6):1045-52, DOI: 10.1016/j.jelectrocard.2015.08.009
[R2] Bond R, Finlay D, Guldenring D, Breen C. Data driven computer simulation to analyse an ECG limb lead system used in connected health environments. Methods of information in medicine. 2016 Apr 20;55(3):258-65. DOI: 10.3414/ME15-01-0120
[R3] Bond RR, Finlay DD, Nugent CD, Breen C, Guldenring D, Daly MJ. The effects of electrode misplacement on clinicians’ interpretation of the standard 12-lead electrocardiogram. European Journal of Internal Medicine. 2012 Oct 1;23(7):610-5, DOI: 10.1016/j.ejim.2012.03.011
[R4] Bond RR, Finlay DD, McLaughlin J, Guldenring D, Cairns A, Kennedy A, Deans R, Waldo AL, Peace A. Human factors analysis of the CardioQuick Patch®: A novel engineering solution to the problem of electrode misplacement during 12-lead electrocardiogram acquisition. Journal of Electrocardiology. 2016 Nov 1;49(6):911-8. DOI: 10.1016/j.jelectrocard.2016.08.009
[R5] Torney, H, O'Hare, P, Davis, L, Delafont, B, Bond, R, McReynolds, H, McLister, A, McCartney, B, Di Maio, R and McEneaney, D, 2016. A usability study of a critical man–machine interface: Can layperson responders perform optimal compression rates when using a public access defibrillator with automated real-time feedback during cardiopulmonary resuscitation?
IEEE Transactions on Human-Machine Systems, 46(5), 749-754, DOI: 10.1109/THMS.2016.2561267
[R6] Bond RR, Novotny T, Andrsova I, Koc L, Sisakova M, Finlay D, Guldenring D, McLaughlin J, Peace A, McGilligan V, Leslie SJ. Automation bias in medicine: The influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. Journal of Electrocardiology. 2018 Aug 10, DOI: https://doi.org/10.1016/j.jelectrocard.2018.08.007
Indicators of research quality
The above journal articles have been subject to blind peer review practice by internationally based editorial boards. [R4] was associated with research presented at the 41st Annual Conference of the International Society for Computerised Electrocardiography where it won
best poster presentation prize (awarded to Bond in 2016). [R5] relates to AED data analysis research involving Bond and Torney which later received the 2019 Paul Dudley White International Scholar award at the Resuscitation Science Symposium in 2019 to recognise
Torney et al.’s research as the highest ranked research that was submitted from the UK. The following grants supported or followed the research presented in R1-R6:
Awarded to: Nugent (PI), Finlay. Grant title: Cross-border Centre for Intelligent Point-of-Care Sensors. Sponsor: NI-Department of Employment and Learning. Period: 2008-2011. Value: GBP1,991,283
Awarded to: Bond (PI), Grant title: Eye Tracking Technology during Clinical 12-lead ECG Interpretation: Where do Expert Cardiologists look? Sponsor: Royal Irish Academy. Period: 2013. Value: GBP1,760
Awarded to: Finlay (PI), Bond, Nugent, Breen and Moore. Grant title: Improving Clinical Practice with the Introduction of Modern Teaching Tools for an Old Science. Sponsor: Higher Education Academy. Period: 2011-2012. Value: GBP2,500
Awarded to: Bjourson (PI), Coates, Maguire, Wong-Lin, Prasad, Bond, Mc Clean, Mc Gilligan, Coyle. Grant title: Centre for Personalised Medicine: Clinical Decision Making and Patient Safety (CPM). Sponsor: EU INTERREG VA. Period: 2017-2022.
Value: Overall Grant £7,343,563.45 - Ulster apportionment GBP3,642,928 . Note: This is the most recent grant that supported the research on automation bias. The work was carried out in the cardiovascular research cluster which is one of a number of clusters funded by this large grant.
4. Details of the impact
I1 – ECG algorithm for computing biomarkers This impact directly follows from [R1]. The algorithm has now been fully adopted since our
final 2015 publication and is being used to compute ECG biomarkers in drug trials [C1]. The algorithm is being used to compute features such as the J-T peak and the QRS-T angle which
are regarded as important for predicting mortality/morbidity and adverse side-effects in drug trials. According to the FDA, Ulster’s algorithm is being used to predict “the risk of drug-induced abnormal heart rhythms”. To date, the FDA has used Ulster’s algorithm [text removed for publication] to detect adverse cardiac side-effects ensuring patient safety and drug quality. While FDA regulatory constraints place an embargo upon the surrounding specific drug trials, we are able to clearly demonstrate that Ulster’s research has impacted upon the practice of a large regulatory organization (FDA) which has adopted Ulster’s algorithm for routine use.
I2 – Medical training This impact directly followed [R2] and [R3]. Having developed a simulator that allows users to see the effects of ECG recording errors, it was shown to be a useful educational tool [C3], and was hence integrated into a medical textbook (12th edition Marriott's Practical Electrocardiography, first established in 1954) and [text removed for publication] [C2] resulting in gross revenue of [text removed for publication] (2014 to 2019). Also, one educator created an online video using the simulator receiving approximately 7,828 views since August 2018 [C4].
I3 – Validated medical devices This impact directly follows [R2-R5]. Ulster’s work in [R5] with HeartSine involved validating the user performance of a novel defibrillator inbuilt with automated audio-visual feedback. From
this work, HeartSine went on to gain regulatory approval for a first-of-kind medical device (AED)
that uses advanced feedback technology to guide users to perform CPR. This device is now available on the market, and has been approved [text removed for publication]. According to HeartSine, sales of these devices have [text removed for publication] since the study involving Ulster. Feedback from a paramedic who recently used the device to save a life said that: “I have been a Paramedic for 30 years.… I recommend this AED daily to anyone that asks” [C7]. Ulster’s research in [R2] and [R3] on the effects of ECG electrode misplacement errors on ECG signals was highlighted by the U.S. Department of Health [C5] and identified by a medical company (eNNOVEA Medical) [C6]. Ulster collaborated with eNNOVEA Medical and helped demonstrate that its device (CardioQuick Patch) reduces electrode placement errors [R4] as well as improving ECG reproducibility (since patients often require multiple recordings). The solution (CardioQuick Patch) is available on the market: Quote from [text removed for publication] at eNNOVEA Medical: “it was this time last year that we kicked off the … CardioQuick Patch®:
… study. Since its presentation as a poster at ISCE and publication in the Journal of
Electrocardiology we have engaged over 15 new hospitals including prestigious institutions such as UPMC and University Hospitals Cleveland Medical Centre as well as private industry such as Eli Lilly Pharmaceuticals. In addition, we have entered into negotiations with a major ECG instrument manufacturer to have us OEM the CardioQuick Patch for sale by their field force. Much of this would not have happened so rapidly if it were not for the research, manuscript development and presentation you and the university were part of. In the 30 years of device development and new product launches, I can say that working with you… was the most fruitful and delightful experience I have had. ….”
I4 – Informing standards This directly follows [R1] and [R6]. Ulster’s ECG algorithm as adopted by the FDA has informed a draft standard and associated technical reports in 2018 [C1], namely IEC 80601-2-86 (medical electrical equipment). The FDA has included a description of Ulster’s algorithm in these documents [C1]. Ulster’s work on [ R6] regarding automation bias of ECG algorithms has been
cited and used to inform the working IEEE standard (P7003) on algorithmic bias in 2018 [C8].
Our work on ECG data structures informed the development of PDF-ECG [C9, C10] in collaboration with companies such as AMPS, Phillips Medical, Mortara Instrument and GE Healthcare. PDF-ECG is a novel electronic solution that preserves the raw data as well as presenting an image of the ECG. PDF-ECG was used as a proof of concept at Fondazione
Poliambulanza Hospital (Brescia, Italy).
5. Sources to corroborate the impact
C1 – Statement of fact in the form of an official letter from the [text removed for publication], US FDA. C2 – Statement of fact in the form of an official letter from the book publisher, [text removed for publication], Wolters Kluwer. The letter also confirms that the publisher has used the simulator to create educational videos to illustrate the effects of electrode misplacement.
C3 – Report evidencing use of the simulator within Ulster University along with quantified evidence of its impact on student learning (note: this is a 2012 document and is used to demonstrate 2012 impact but evidences the initiation of the simulator which impacted on a textbook).
C4 – Medical training video using Ulster’s simulator created by an instructor of Basic Life Support and Advanced by American Heart Association. C5 – US Department of Health’s Patient Safety Network logging the research on ECG electrode misplacement errors.
C6 – Electronic communication from [text removed for publication] at eNNOVEA Medical. C7 – Statement of fact in the form of a letter from the [text removed for publication] at HeartSine
Technologies Ltd (now acquired by Stryker).
C8 – Official Letter from the [text removed for publication] IEEE P7003 standard Algorithmic Bias and Considerations working group.
C9 – Publication of PDF-ECG in clinical practice: A model for long-term preservation of digital 12-lead ECG data. C10 – Official letter from the [text removed for publication], an international medical company providing ECG analysis software tools.
- Submitting institution
- University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Summary impact type
- Technological
- Is this case study continued from a case study submitted in 2014?
- No
1. Summary of the impact
Ulster’s research on digital solutions for self-management of health conditions led to novel software architectures, communications infrastructure, sensing technologies and eHealth apps, resulting in a number of impacts including:
I1 Establishment of a free-to-use, national, IoT communications platform (LPWAN-NI) accessed by >130 companies to develop approximately 19 products/services.
I2 Development of an open software infrastructure to underpin the collection of sensor data for
self-management solutions in Northern Ireland and Italy.
I3 Creation of a self-management product, incorporating behaviour change strategies, for an SME in Northern Ireland, currently being sold in 22 countries.
2. Underpinning research
The case study is underpinned by research from the 2008 SMART2 Project (EP/F001916/1) which investigated the potential of technology to support self-management of long-term health conditions. Ulster (Davies, Nugent, Zheng & McCullagh) was the lead technology partner in the project which, for the first time, moved beyond a focus on symptom monitoring to incorporate aspects of rehabilitation, behavioural change and education.
These technologies have been diversified and applied to the management of other long-term conditions including Chronic Obstructive Pulmonary Disorder, Pain, Stroke [R3], Dementia [R5-R6], Sedentarism, Cerebral Palsy [R4] and Autism [R1]. This research increasingly combined novel approaches for the collection and processing of sensor data with psychological behavioural change models incorporating gamification strategies [R4] and goal setting [R5]. These technologies support fundamental computer science research and facilitated the development of a suite of comprehensive technological self-management solutions.
Significant research advances that underpin the impacts [I1-I3] are clustered in three main topics and mapped to references [R1-R6] and supported by corroborating evidence [C1-C9].
The links between underpinning research and the impact are illustrated in Fig.1.
Transmission, collection, processing, and storage of data from sensors/self-reported information [R1 & R2].
These solutions rely on the ability to collect and aggregate quantitative and qualitative data from wearable, mobile, ambient and health sensors, in addition to self-reported information.
This research developed a family of generic data storage and processing platforms which can scale from small, embedded solutions to server/cloud deployments and hyperscale cloud/edge computing (Rafferty, Cruciani and Paggetti). This research [R1-R2] has provided the data aggregation solutions in [I1] and [I2]. The platform is an open, reusable framework providing core infrastructure to allow authorised clients to share any data, regardless of format. These frameworks include the ability to collect data from low-power, long-range communications solutions such as a Low Power Wide Area Network (LPWAN).
Learning behavioural patterns and providing personalisation and context awareness [R1, R3, R4].
Ulster research (led by Nugent) used emerging sensing solutions for applications such as tracking gait parameters and postural imbalance [R3], measuring physical activity in children using mobility aids [R4] and treatment of children with Autism [R1]. This research resulted in the design, implementation, and evaluation of techniques to process sensor information and extract accurate measures of behaviour. These insights provided personalised interventions to the user [R3].
Enabling effective and persuasive feedback for behaviour change [R3-R6].
These solutions increasingly incorporate behaviour change techniques, including gamification
[R4], self-monitoring [R5] and goal setting [R3, R6]. The evaluation of these solutions provided insight into how technology can be designed to best engage users. The Gray Matters project demonstrated the effectiveness of technology-based interventions on clinical outcomes for prevention of Alzheimer’s. Disease [R5]. Through a randomised control trial (n=146) the results demonstrated that consistency of engagement was important, participants who engaged most with the app showing the best improvements in clinical measures. Similarly, Cleland and Ennis demonstrated the use of technology to increase physical activity through gamification [R4]. The project used accelerometers, integrated in a mobility aid, to quantify
steps and pedal revolutions. This data was used to drive an avatar towards a personalised goal. The user was rewarded with virtual badges based on their performance.
3. References to the research
Outputs can be provided by Ulster University on request.
R1 Lucia Billeci, Alessandro Tonacci, Gennaro Tartarisco, Antonio Narzisi, Simone Di Palma, Daniele Corda, Giovanni Baldus, Federico Cruciani, and Michelangelo Study Group (incl. Mark Donnelly). "An integrated approach for the monitoring of brain and autonomic response of children with autism spectrum disorders during treatment by wearable technologies." Frontiers in Neuroscience 10 (2016): 276.
R2 Joseph Rafferty, Jonathan Synnott, Chris D. Nugent, Andrew Ennis, Phillip A. Catherwood, Ian McChesney, Ian Cleland, Sally McClean. “A scalable, research oriented, generic, sensor data platform.’ IEEE Access 6 (2018): 45473-45484.
R3 Richard John Davies, Jack Parker, Paul McCullagh, Huiru Zheng, Chris Nugent, Norman David Black, and Susan Mawson. "A personalized self-management rehabilitation system for stroke survivors: a quantitative gait analysis using a smart insole." JMIR rehabilitation and assistive technologies 3, no. 2 (2016): e11.
R4 Andrew Ennis, Ian Cleland, Chris Nugent, Laura Finney, David Trainor & Aidan Bennett (2016, November). The Use of Gamification Techniques in a Clinical Setting for the Collection of Longitudinal Kinematic Data. In International Conference on Ubiquitous Computing and Ambient Intelligence (pp. 267- 273). Springer, Cham.
R5 Phillip J. Hartin, Chris D. Nugent, Sally I. McClean, Ian Cleland, JoAnn T. Tschanz, Christine J. Clark, and Maria C. Norton. "The empowering role of mobile apps in behavior change interventions: The Gray Matters randomized controlled trial." JMIR mHealth and uHealth 4, no. 3 (2016): e93.
R6 Timothy Patterson, Federico Cruciani, Ian Cleland, Chris D. Nugent, Norman D. Black, Paul J. McCullagh, Huiru Zheng, Mark P. Donnelly, Suzanne McDonough, and Adele Boyd. "KeepWell: a generic platform for the self-management of chronic conditions." In XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, pp. 897-902. Springer, Cham, 2016.
Indicators of research quality
[R1- R6] have been peer reviewed by internationally-based editorial boards of the relevant
journals.
Key funding
Grants which have supported or followed the research in [R1-R6] include:-
Black ND, McCullagh PJ, Nugent CD, Zheng H, SMART 2: Self-Management supported by Assistive, Rehabilitation and Telecare Technologies, EPSRC, EP/F001916, 01/2008-06/2012, GBP566,653.
Galway, L., Nugent, C., Donnelly, M., McCullagh, P., Lightbody, G. & Chen, L. MICHELANGELO: Patient-centric Model for Remote Management, Treatment and Rehabilitation of Autistic Children. CEC - Framework 7. 01/10/2011-31/03/15. GBP202,058.
Mc Donough, S., Black, N., McCullagh, P., Zheng, H., Nugent, C., Finlay, D. & Donnelly, M. Self-Management Platform for Connected Health, Invest NI. 01/04/13-31/07/18. GBP482,584.
Nugent C. & McLaughlin J., Connected Health Innovation Centre, InvestNI. 01/06/13 29/02/20. GBP4,531,383.
Nugent C, McCullagh P, Breslin G and Cleland I. ACROSSING - Advanced Technologies and Platform for Smarter Assistive Living, CEC-H2020-MSCA-ITN, 01/01/16 – 31/12/19. GBP203,014.
Cleland I., Nugent C., University of Ulster, James Leckey Design Limited Ref: KTP010295, Innovate UK Knowledge Transfer Partnerships, Innovate UK. 29/02/16-31/05/19. GBP134,000.
4. Details of the impact
I1 Establishment of a free to use, national, IoT communications platform (LPWAN) accessed by >130 companies.
In 2018 Ulster established a free-to-use, national, IoT communications platform (LPWAN-NI). LPWAN-NI offers a low-power, wireless, wide-area, network for development of IoT solutions
[R2].
LPWAN-NI consists of over 60 gateways covering the geography of Northern Ireland including hard to reach border regions. LPWAN-NI has been accessed by over 130 companies and
approximately 17M messages have been sent via the network since its establishment in 2018
[C1]. This has been independently verified by Digital Catapult [C1]. Ulster was awarded
GBP185,000 in funding to support local start-ups with the aim of producing new products leveraging the network [C1]. 19 new business ventures have been created, and large public service providers such as [text removed for publication] are using the network to monitor essential equipment [C2]. One exemplar of companies benefiting from the network is [text removed for publication] [C2]. [text removed for publication] now employs 3 members of staff (headcount 3, 2.5 FTE) and has been established to drive novel solutions to market based on LPWAN technology and University expertise [C2].
I2 Development of open software infrastructure to underpin the collection of sensor data
for self-management solutions in Northern Ireland and Italy.
Collection of qualitative and quantitative healthcare data lends itself to a wide range of applications including psychology, sports, health and wellbeing. Researchers at Ulster have produced an open, scalable, and flexible software architecture for the collection and sharing of such data [R1-R2]. Variations of this architecture have been adopted by the SMEs [text removed for publication] and [text removed for publication]. Since 2016, [text removed for publication] has been using the data aggregator, which collects data from health devices, mobile apps and self-reporting, as a self-management platform for various Chronic conditions [text removed for publication]. It has confirmed that the integration of Ulster research into its product portfolio has led to the following impact [C3]:
Development of a new self-management app for long term chronic conditions.
Used the aggregator to secure contracts of over EUR130,000 (10-2020).
Established collaborations with more than 15 international partners.
The software architecture has additionally been utilised by [text removed for publication], to collect data from sensor technology in a care home and prison environment. The solution was the subject of two Invention disclosures within Ulster [C4]. The solution was subsequently licensed for evaluation by [text removed for publication] on 23/08/2017 [C5]. Since licensing this technology, [text removed for publication] has diversified its company from security, establishing a health division [text removed for publication].
I3 Creation of a self-management product, incorporating behaviour change strategies, for an SME in Northern Ireland, currently being sold in 22 countries.
Ulster’s research into the design, development and evaluation of self-management technologies, specifically processing for sensor data for behavioural insights [R3-R4] and the development of health apps [R5-R6], has directly led to the creation of new products for SME [text removed for publication]
Following the underpinning research [R3-R6], knowledge was transferred to the company through a KTP project. The impact of this research partnership has been acknowledged by the Senior Knowledge Transfer Adviser from KTN [C8]. The partnership was also rated as “Outstanding” by a panel of independent reviewers as part of the KTN assessment [C9]: “This project has embedded a very significant new capability into the business, which has allowed the company to capture clinically important data (KTN, Knowledge partnership adviser)” [C8].
[text removed for publication] consists of a connected sensor that quantifies movement and a mobile app that contains elements of goal setting and gamification to encourage participation
in physical activity.Both measurement and gamification were investigated within the fundamentalresearch [R4] and taken forward through the KTP. This is the first software enhanced product to be offered by [text removed for publication] [C6]. The first product in this range, [text removed for publication], was launched in December 2019 and is currently being marketed in 22 countries, including the UK, Europe, Middle East, and new markets in Australia. [text removed for publication] have been delivered with 25 demo units produced and sales of approximately [text removed for publication]. In [text removed for publication] 2020 [text removed for publication] was acquired by [text removed for publication]. The innovation, as demonstrated by product lines such as the [text removed for publication], has been critical in attracting such an acquisition. [text removed for publication] have been impressed by the innovative approach to research and development at [text removed for publication] and are keen to grow the [text removed for publication] of their business with [text removed for publication] in Northern Ireland” [C6].
It is estimated that over 100 children have already positively benefited from using the product. A case study with a child using the device, his parents and care team has highlighted the impact this technology has on children’s quality of life. Physios have reported improvements in standing time from 20 to 48 seconds in 8 weeks. Classroom assistants have reported that [text removed for publication] is now able to stand up much straighter and needs less help in and out of his wheelchair than before” [C7].
5. Sources to corroborate the impact
C1: Letter from Digital [text removed for publication] confirming the reach and significance for LPWAN.
C2: Testimonial letter from [text removed for publication] corroborating the impact of LPWAN on
their business.
C3: Testimonial from [text removed for publication] corroborating the integration of Ulster research within the company and summarising the related impact of this.
C4: Invention Disclosure forms for the Behaviour Monitoring solution.
C5: Licensing Agreement with [text removed for publication] demonstrating the technologies which have been licensed from Ulster.
C6: Testimonial from [text removed for publication] demonstrating the integration of Ulster research
into their [text removed for publication] product.
C7: Marketing Material from [text removed for publication], including case study with child who has used the device over an 8-week period.
C8: Letter from Knowledge Transfer Network highlighting the impact of the partnership with [text removed for publication].
C9: Assessment of Knowledge Transfer partnership letter from Innovate UK showing the project as being rated “Outstanding”.