A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 926
- Type
- D - Journal article
- DOI
-
10.1109/JIOT.2018.2846359
- Title of journal
- IEEE Internet of Things
- Article number
- -
- First page
- 1384
- Volume
- 6
- Issue
- 2
- ISSN
- 2327-4662
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work describes a technique to detect more physical activity in a gymnasium using accelerometers in wearable devices. A two layer recognition framework is proposed that can classify aerobic, sedentary, and free weight activities, count repetitions and sets for the free weight exercises, and measure quantities of repetitions and sets for free weight activities. The results indicate that the proposed framework has better performance in recognizing and measuring activity than other approaches. This paper has been cited in fields such as health, mobile computing and artificial intelligence.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
