Aerobic Exercise Recognition Through Sparse Representation Over Learned Dictionary by Using Wearable Inertial Sensors

Aerobic exercise is conducive to reducing the risks of cardiovascular disease and central arterial stiffness. However, it can also cause some health hazards (such as tissue oxidative damage), especially for the elderly. It is essential to recognize and monitor different aerobic exercises for the hea...

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Bibliographic Details
Published in:Journal of medical and biological engineering 2018-08, Vol.38 (4), p.544-555
Main Authors: Guo, Ming, Wang, Zhelong, Yang, Ning
Format: Article
Language:English
Subjects:
Activity recognition
Aerobics
Algorithms
Bayesian analysis
Biomedical Engineering and Bioengineering
Cell Biology
Correlation coefficients
Dictionaries
Engineering
Exercise
Geriatrics
Health hazards
Imaging
Inertial sensing devices
Machine learning
Older people
Original Article
Physical fitness
Radiology
Representations
Sensors
Sporting goods
Stiffness
Support vector machines
Thigh
Wrist
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Summary:Aerobic exercise is conducive to reducing the risks of cardiovascular disease and central arterial stiffness. However, it can also cause some health hazards (such as tissue oxidative damage), especially for the elderly. It is essential to recognize and monitor different aerobic exercises for the health of exercisers. In this paper, a multi-sensor monitoring system is established for aerobic exercise recognition, and a novel recognition algorithm based on dictionary learning algorithm and sparse representation is proposed. Eight volunteers are invited to carry out ten activities, and five wireless inertial sensor nodes are used to collect the sensor data. Several experiments are implemented to verify the effectiveness of the recognition algorithm proposed in the paper. According to the experimental results, our method achieves the best performance than four other recognition algorithms including decision tree C4.5, naive Bayes, support vector machine and sparse representation. Besides, the other two aspects are also studied in the paper, one is the effect of different binding positions of sensors on classification results, and the other is the effect of selecting different features. The results of the experiments show that two sensor nodes attached to the right wrist and the left thigh achieve better result, and the feature “correlation coefficient” is not important to recognize different aerobic exercises that are investigated in our paper.
ISSN:1609-0985
2199-4757
DOI:10.1007/s40846-017-0327-0