Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor
Abstract
In this paper, we present a real-time algorithm for automatic recognition of not only physical activities, but also, in some cases, their intensities, using five triaxial wireless accelerometers and a wireless heart rate monitor. The algorithm has been evaluated using datasets consisting of 30 physical gymnasium activities collected from a total of 21 people at two different labs. On these activities, we have obtained a recognition accuracy performance of 94.6% using subject-dependent training and 56.3% using subject-independent training. The addition of heart rate data improves subject-dependent recognition accuracy only by 1.2% and subject-independent recognition only by 2.1%. When recognizing activity type without differentiating intensity levels, we obtain a subject-independent performance of 80.6%. We discuss why heart rate data has such little discriminatory power.
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Tapia et al. generates 80.6% accuracy for 30 activities, including lifting weights, doing push-ups and rowing.
“Tapia et al. is another HAR system that generates 80.6% accuracy for 30 activities, including lifting weights, doing push-ups and rowing [159].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [159]
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- Authors
- Tapia, E. M., Intille, S. S., Haskell, W., Larson, K., Wright, J., King, A., & Friedman, R.
- Affiliations
- Massachusetts Institute of Technology (US)
- Stanford University (US)
- Boston Medical Center (US)
- Venue
- 2007 11th IEEE International Symposium on Wearable Computers
- Publisher
- IEEE
- Published
- 2007-10
- Pages
- 1-4
- ISSN
- 1550-4816 (ISSN-L)
- OpenAlex
- W2170724010
- Cited by
- 458
- References
- 10
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 16 · 2023: 16 · 2022: 24 · 2021: 20 · 2020: 32 · 2019: 30 · 2018: 29 · 2017: 38 · 2016: 38 · 2015: 43 · 2014: 28 · 2013: 28 · 2012: 27
- FWCI
- 12.59
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings (publishedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- physical activity recognition, activity intensity classification, wireless accelerometers, triaxial accelerometry, heart rate monitoring, real-time recognition, subject-independent recognition, multimodal sensor fusion
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