TY - GEN
T1 - GIFT
T2 - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
AU - Akpa, Elder A.H.
AU - Fujiwara, Masashi
AU - Arakawa, Yutaka
AU - Suwa, Hirohiko
AU - Yasumoto, Keiichi
N1 - Funding Information:
This work was partly supported by JSPS KAKENHI Grant Number 26700007.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/2
Y1 - 2018/10/2
N2 - It has been intensively demonstrated that physical activity can enhance the mental and physical health of practitioners. In recent years, fitness activities became the most common way to engage in physical activities. In this paper, we propose a smart-glove based fitness activity tracking system that can detect athletes activities in any indoor fitness facility, with no need of attaching multiple sensors on the athlete's body. The system adopts force sensitive resistor (FSR) sensors to identify the type of exercise by analyzing the pressure distribution in the hand palm during fitness activities. To evaluate the performance of our proposed system, we ran a pilot study with 10 healthy participants over 10 common fitness activities. The experimental results showed an overall recognition accuracy rate of 87%. We believe the promising results would contribute to the works on personal assisted coaching systems and create enjoyable experiences when performing fitness activities.
AB - It has been intensively demonstrated that physical activity can enhance the mental and physical health of practitioners. In recent years, fitness activities became the most common way to engage in physical activities. In this paper, we propose a smart-glove based fitness activity tracking system that can detect athletes activities in any indoor fitness facility, with no need of attaching multiple sensors on the athlete's body. The system adopts force sensitive resistor (FSR) sensors to identify the type of exercise by analyzing the pressure distribution in the hand palm during fitness activities. To evaluate the performance of our proposed system, we ran a pilot study with 10 healthy participants over 10 common fitness activities. The experimental results showed an overall recognition accuracy rate of 87%. We believe the promising results would contribute to the works on personal assisted coaching systems and create enjoyable experiences when performing fitness activities.
UR - http://www.scopus.com/inward/record.url?scp=85056465946&partnerID=8YFLogxK
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U2 - 10.1109/PERCOMW.2018.8480211
DO - 10.1109/PERCOMW.2018.8480211
M3 - Conference contribution
AN - SCOPUS:85056465946
T3 - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
SP - 52
EP - 57
BT - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 March 2018 through 23 March 2018
ER -