TY - GEN
T1 - A contactless sport training monitor based on facial expression and remote-ppg
AU - Wu, Bing Fei
AU - Lin, Chun Hsien
AU - Huang, Po Wei
AU - Lin, Tzu Min
AU - Chung, Meng Liang
N1 - Funding Information:
This work is supported by the Ministry of Science and Technology under Grand no. MOST 105-2221-E-009-041
Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/27
Y1 - 2017/11/27
N2 - To successfully increase athletes' or exercisers' fitness and endurance, the factors of physiological signal, emotion, or the level of fatigue should be considered during the training program. Many clinical decision support systems can assist to monitor the exercisers by some wearable devices. And, the questionnaire should also be taken into account to produce a report. Such process is cumbersome, and the results are not objective. Furthermore, one may feel uncomfortable when wearing the devices during the training program. In this research, the Rating of Perceived Exertion (RPE) is expected to be estimated automatically without any wearable devices and questionnaires. A camera based heart rate detection algorithm nd a fatigue expression feature extractor are fused to estimate the RPE value. The results show that our heart rate detection algorithm can be competitive to the wearable devices, and the trend of the detected heart rate is correlated to RPE. Moreover, the fatigue feature can help reduce the error of the estimation.
AB - To successfully increase athletes' or exercisers' fitness and endurance, the factors of physiological signal, emotion, or the level of fatigue should be considered during the training program. Many clinical decision support systems can assist to monitor the exercisers by some wearable devices. And, the questionnaire should also be taken into account to produce a report. Such process is cumbersome, and the results are not objective. Furthermore, one may feel uncomfortable when wearing the devices during the training program. In this research, the Rating of Perceived Exertion (RPE) is expected to be estimated automatically without any wearable devices and questionnaires. A camera based heart rate detection algorithm nd a fatigue expression feature extractor are fused to estimate the RPE value. The results show that our heart rate detection algorithm can be competitive to the wearable devices, and the trend of the detected heart rate is correlated to RPE. Moreover, the fatigue feature can help reduce the error of the estimation.
KW - Clinical decision support system
KW - Facial expression recognition
KW - Heart rate detection
UR - http://www.scopus.com/inward/record.url?scp=85044172039&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044172039&partnerID=8YFLogxK
U2 - 10.1109/SMC.2017.8122715
DO - 10.1109/SMC.2017.8122715
M3 - Conference contribution
AN - SCOPUS:85044172039
T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
SP - 846
EP - 851
BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Y2 - 5 October 2017 through 8 October 2017
ER -