A Smart Glove to Track Fitness Exercises by Reading Hand Palm

Medical studies have intensively demonstrated that sports activity can enhance both the mental and the physical health of practitioners. In recent years, fitness activity became the most common way to motivate and engage people in sports activity. Recently, there have been multiple attempts to elabo...

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Bibliographic Details
Published in:Journal of sensors 2019-01, Vol.2019 (2019), p.1-19
Main Authors: Arakawa, Yutaka, Suwa, Hirohiko, Fujiwara, Masashi, Akpa, A. H. Elder, Yasumoto, Keiichi
Format: Article
Language:English
Subjects:
Accuracy
Activity recognition
Algorithms
Bioinformatics
Biomechanics
Bodybuilding
Counting
Exercise
Gene expression
Gloves
Hands
International conferences
Palm
Physical fitness
Pressure distribution
Sensors
Sports training
Strength training
Stress concentration
Wearable computers
Workshops
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Summary:Medical studies have intensively demonstrated that sports activity can enhance both the mental and the physical health of practitioners. In recent years, fitness activity became the most common way to motivate and engage people in sports activity. Recently, there have been multiple attempts to elaborate on the “ideal” IoT-based solution to track and assess these fitness activities. Most fitness activities (except aerobic activities like running) involve one or multiple interactions between the athlete’s hand palms and body or between the hand palms and the workout materials. In this work, we present our idea to exploit these biomechanical interactions of the hand palms to track fitness activities via a smart glove. Our smart glove-based system integrates force-sensitive resistor (FSR) sensors into wearable fitness gloves to identify and count fitness activity, by analyzing the time series of the pressure distribution in the hand palms observed during fitness sessions. To assess the performance of our proposed system, we conducted an experimental study with 10 participants over 10 common fitness activities. For the user-dependent activity recognition case, the experimental results showed 88.90% of the F score for overall activity recognition. The result of leave-one-participant-out cross-validation showed an F score ranging from 58.30% to 100%, with an average of 82.00%. For the exercise repetition count, the system achieved an average counting error of 9.85%, with a standard deviation of 1.38.
ISSN:1687-725X
1687-7268
DOI:10.1155/2019/9320145