フィットネス識別用のスマートグローブに関する研究がJournal of Sensorsに採択されました。
Akpa, Elder A. H.; Suwa, Hirohiko; Arakawa, Yutaka; Yasumoto, Keiichi; Fujiwara, Masashi
A Smart Glove to Track Fitness Exercises by Reading Hand Palm Journal Article
In: Journal of Sensors, 2019.
@article{Akpa2019,
title = {A Smart Glove to Track Fitness Exercises by Reading Hand Palm},
author = {Elder A. H. Akpa and Hirohiko Suwa and Yutaka Arakawa and Keiichi Yasumoto and Masashi Fujiwara},
url = {https://www.hindawi.com/journals/js/2019/9320145/},
doi = {10.1155/2019/9320145},
year = {2019},
date = {2019-05-21},
journal = { Journal of Sensors},
abstract = {Medical studies have intensively demonstrated that sports activity can enhance both the mental and 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 bio-mechanical 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 F-score for overall activity recognition. The result of leave-one-participant-out cross-validation showed 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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Medical studies have intensively demonstrated that sports activity can enhance both the mental and 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 bio-mechanical 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 F-score for overall activity recognition. The result of leave-one-participant-out cross-validation showed 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.
In this work, we present our idea to exploit these bio-mechanical 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 F-score for overall activity recognition. The result of leave-one-participant-out cross-validation showed 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.