Conference proceeding
Automatic classification of physical exercises from wearable sensors using small dataset from non-laboratory settings
2017 IEEE Life Sciences Conference (LSC), Vol.2018, pp.111-114
IEEE Life Sciences Conference (LSC) (Sydney, NSW, Australia, 13/12/2017 - 15/12/2017)
12/2017
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Abstract
Effective classification of physical exercises allows individuals to assess their levels of physical activity and functional ability for maintaining physical fitness and help reduce risks of chronic diseases. This paper investigates and compares classification techniques for detecting physical exercise in real-world contexts that often only supports a small training dataset. The system combines heart rate with other exercise-related features, such as distance, duration, calories, etc. The experiment uses a dataset of 40 realistic (uncontrolled) sessions from 22 individuals wearing wearable sensors while performing different exercises, including walking, aerobics, running, indoor cycling, and weight training. Based on a 5-fold cross validation approach, AdaBoost demonstrated the highest (87.25%) classification accuracy compared to other classifiers, including support vector machine, neural network, and binary decision tree when used individually. When fused together at the decision level using majority-voting techniques, these classifiers achieved higher accuracy (89.25%) than that of individual applications.
Details
- Title
- Automatic classification of physical exercises from wearable sensors using small dataset from non-laboratory settings
- Creators
- Alok Kumar Chowdhury - Queensland University of Technology, Science and Engineering Faculty, Brisbane, AustraliaAleksandr Farseev - National University of Singapore, LMS lab, School of computing, SingaporePrithwi Raj Chakraborty - Queensland University of Technology, Science and Engineering Faculty, Brisbane, AustraliaDian Tjondronegoro - Southern Cross University, School of Business and Tourism, AustraliaVinod Chandran - Queensland University of Technology, Science and Engineering Faculty, Brisbane, Australia
- Publication Details
- 2017 IEEE Life Sciences Conference (LSC), Vol.2018, pp.111-114
- Conference
- IEEE Life Sciences Conference (LSC) (Sydney, NSW, Australia, 13/12/2017 - 15/12/2017)
- Publisher
- IEEE
- Identifiers
- 991012927079802368
- Academic Unit
- Faculty of Science and Engineering; Information Technology
- Language
- English
- Resource Type
- Conference proceeding