Gym Exercises Monitoring with Smart Gloves: Exercise Recognition, Repetition Counting, and Imbalance Quantification
Pages 259 - 266
Abstract
Fitness training attracts a lot of attention, as it tumbles the health risks in a participant. Most fitness training activities involve the interaction of hand and workout equipment. Therefore, we present a pressure-sensing smart glove system to recognize fitness exercises, count repetitions, and quantify imbalance. The proposed smart glove with 93 sensing points at the wrist and palm can attain pressure-distributed time series data. We evaluate the system’s performance using the collected exercise data from 20 participants. Support vector classifier attains overall 96 accuracy of exercise recognition. For counting repetitions, the overall mean absolute error of 1.78 is calculated for ten types of exercises. Using the ratio index technique, we compute the upper-threshold (+0.6) and lower-threshold (-0.6) to quantify the imbalance of lifted weights.
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Index Terms
- Gym Exercises Monitoring with Smart Gloves: Exercise Recognition, Repetition Counting, and Imbalance Quantification
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BIBE2021: The Fifth International Conference on Biological Information and Biomedical EngineeringGym exercise has become a focus of attention nowadays because of its health benefits. Automatic gym exercise recognition is an emerging research field which aimed at guiding people to keep fit scientifically through gym exercise monitoring. However, as ...
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February 2023
619 pages
ISBN:9781450398411
DOI:10.1145/3587716
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Published: 07 September 2023
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ICMLC 2023
ICMLC 2023: 2023 15th International Conference on Machine Learning and Computing
February 17 - 20, 2023
Zhuhai, China
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