TY - GEN
T1 - HearFit
T2 - 40th IEEE Conference on Computer Communications, INFOCOM 2021
AU - Xie, Yadong
AU - Li, Fan
AU - Wu, Yue
AU - Wang, Yu
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/10
Y1 - 2021/5/10
N2 - Fitness can help to strengthen muscles, increase resistance to diseases and improve body shape. Nowadays, more and more people tend to exercise at home/office, since they lack time to go to the dedicated gym. However, it is difficult for most of them to get good fitness effect due to the lack of professional guidance. Motivated by this, we propose HearFit, the first non-invasive fitness monitoring system based on commercial smart speakers for home/office environments. To achieve this, we turn smart speakers into active sonars. We design a fitness detection method based on Doppler shift and adopt the short time energy to segment fitness actions. We design a high-accuracy LSTM network to determine the type of fitness. Combined with incremental learning, users can easily add new actions. Finally, we evaluate the local (i.e., intensity and duration) and global (i.e., continuity and smoothness) fitness quality of users to help to improve fitness effect and prevent injury. Through extensive experiments including over 7, 000 actions of 10 types of fitness with and without dumbbells from 12 participants, HearFit can detect fitness actions with an average accuracy of 96.13%, and give accurate statistics in various environments.
AB - Fitness can help to strengthen muscles, increase resistance to diseases and improve body shape. Nowadays, more and more people tend to exercise at home/office, since they lack time to go to the dedicated gym. However, it is difficult for most of them to get good fitness effect due to the lack of professional guidance. Motivated by this, we propose HearFit, the first non-invasive fitness monitoring system based on commercial smart speakers for home/office environments. To achieve this, we turn smart speakers into active sonars. We design a fitness detection method based on Doppler shift and adopt the short time energy to segment fitness actions. We design a high-accuracy LSTM network to determine the type of fitness. Combined with incremental learning, users can easily add new actions. Finally, we evaluate the local (i.e., intensity and duration) and global (i.e., continuity and smoothness) fitness quality of users to help to improve fitness effect and prevent injury. Through extensive experiments including over 7, 000 actions of 10 types of fitness with and without dumbbells from 12 participants, HearFit can detect fitness actions with an average accuracy of 96.13%, and give accurate statistics in various environments.
UR - http://www.scopus.com/inward/record.url?scp=85111945896&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM42981.2021.9488811
DO - 10.1109/INFOCOM42981.2021.9488811
M3 - Conference contribution
AN - SCOPUS:85111945896
T3 - Proceedings - IEEE INFOCOM
BT - INFOCOM 2021 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 May 2021 through 13 May 2021
ER -