TY - GEN
T1 - A posture evaluation system for fitness videos based on recurrent neural network
AU - Liu, An Lun
AU - Chu, Wei Ta
N1 - Funding Information:
ACKNOWLEDGMENT This work was partially supported by Qualcomm Technologies, Inc. under the grant number B109-K027D, and by the Ministry of Science and Technology, Taiwan, under the grant 108-2221-E-006-227-MY3, 107-2923-E-194-003-MY3, and 109-2218-E-002-015.
Publisher Copyright:
© 2020 IEEE
PY - 2020/11
Y1 - 2020/11
N2 - We present a posture evaluation system especially for fitness. Given a fitness video where a user repetitively performs a movement for fitness, we first detect human posture at each video frame. The evolution of posture in consecutive frames is then characterized by a recurrent neural network (RNN). This RNN examines this movement and outputs the degree of goodness (badness). This examination is important for users because prompt inspection of bad movement avoids injury and improves effectiveness of fitness. We demonstrate that the proposed system can accurately detect bad postures when the users perform two movements called Dumbbell Lateral Raise and Biceps Curl. We believe this work is one of the very few studies of using deep neural networks for fitness evaluation.
AB - We present a posture evaluation system especially for fitness. Given a fitness video where a user repetitively performs a movement for fitness, we first detect human posture at each video frame. The evolution of posture in consecutive frames is then characterized by a recurrent neural network (RNN). This RNN examines this movement and outputs the degree of goodness (badness). This examination is important for users because prompt inspection of bad movement avoids injury and improves effectiveness of fitness. We demonstrate that the proposed system can accurately detect bad postures when the users perform two movements called Dumbbell Lateral Raise and Biceps Curl. We believe this work is one of the very few studies of using deep neural networks for fitness evaluation.
UR - http://www.scopus.com/inward/record.url?scp=85103534847&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103534847&partnerID=8YFLogxK
U2 - 10.1109/IS3C50286.2020.00055
DO - 10.1109/IS3C50286.2020.00055
M3 - Conference contribution
AN - SCOPUS:85103534847
T3 - Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
SP - 185
EP - 188
BT - Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
Y2 - 13 November 2020 through 16 November 2020
ER -