TY - GEN
T1 - Application of a Wearable Physiological Monitoring System in Pulmonary Respiratory Rehabilitation Research
AU - Cao, Desen
AU - Zhang, Zhengbo
AU - Liang, Hong
AU - Liu, Xiaoli
AU - She, Yingjia
AU - Li, Yuzhu
AU - Li, Deyu
AU - Yu, Mengsun
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Pulmonary rehabilitation has been demonstrated as a highly effective and safe treatment for improving health-related quality of life and reducing hospital admissions mortality in chronic obstructive pulmonary disease (COPD) patients. Despite significant progress within the physiological monitoring device industry, the widespread integration of wearable systems into medical practice remains limited. In this paper, we present a medical-grade wearable multi-sensor system to acquire COPD patients' vital signs and assist in pulmonary respiratory rehabilitation. Currently, 4 areas in this field were explored: breathing pattern analysis, respiratory exercises training, six minute walk test and inpatient 24-hours physiological monitoring. Totally 130 subjects enrolled in this study. The results show that this system can acquire cardiopulmonary physiological signals unobtrusively and accurately, and provide useful information for pulmonary respiratory rehabilitation. The next step for this work is to collect more physiological data from COPD patients during respiratory training exercises and generate individualized guideline and therapy for pulmonary respiratory rehabilitation.
AB - Pulmonary rehabilitation has been demonstrated as a highly effective and safe treatment for improving health-related quality of life and reducing hospital admissions mortality in chronic obstructive pulmonary disease (COPD) patients. Despite significant progress within the physiological monitoring device industry, the widespread integration of wearable systems into medical practice remains limited. In this paper, we present a medical-grade wearable multi-sensor system to acquire COPD patients' vital signs and assist in pulmonary respiratory rehabilitation. Currently, 4 areas in this field were explored: breathing pattern analysis, respiratory exercises training, six minute walk test and inpatient 24-hours physiological monitoring. Totally 130 subjects enrolled in this study. The results show that this system can acquire cardiopulmonary physiological signals unobtrusively and accurately, and provide useful information for pulmonary respiratory rehabilitation. The next step for this work is to collect more physiological data from COPD patients during respiratory training exercises and generate individualized guideline and therapy for pulmonary respiratory rehabilitation.
KW - breathing pattern analysis
KW - physiological monitoring
KW - pulmonary rehabilitation
KW - respiratory training
KW - wearable system
UR - https://www.scopus.com/pages/publications/85062828276
U2 - 10.1109/CISP-BMEI.2018.8633113
DO - 10.1109/CISP-BMEI.2018.8633113
M3 - 会议稿件
AN - SCOPUS:85062828276
T3 - Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018
BT - Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018
A2 - Li, Wei
A2 - Li, Qingli
A2 - Wang, Lipo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018
Y2 - 13 October 2018 through 15 October 2018
ER -