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12 results on '"Wang, Runsen"'

2. Analysis and Design of the Shopping Trolley for Elderly People Based on Ergonomics

Author

Song, Yaxuan, Ding, Ding, Wang, Runsen, Di, Hanzheng, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2023
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3. Modular Cleaning Set Design Based on Ergonomics

Author

Zhao, Xiaoxuan, Lu, Min, Wang, Runsen, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2022
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5. Sleep‐phasic heart rate variability predicts stress severity: Building a machine learning‐based stress prediction model.

Author

Fan, Jingjing, Mei, Junhua, Yang, Yuan, Lu, Jiajia, Wang, Quan, Yang, Xiaoyun, Chen, Guohua, Wang, Runsen, Han, Yujia, Sheng, Rong, Wang, Wei, and Ding, Fengfei

Subjects
PLETHYSMOGRAPHY, PREDICTION models, RESEARCH funding, ACCELEROMETERS, SEVERITY of illness index, HEART beat, ELECTROCARDIOGRAPHY, PSYCHOLOGICAL stress, RAPID eye movement sleep, MACHINE learning, SLEEP stages, SENSITIVITY & specificity (Statistics)
Abstract

We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self‐screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24‐h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non‐cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart‐device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning‐based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Correlation of Circadian Rhythms of Heart Rate Variability Indices with Stress, Mood, and Sleep Status in Female Medical Workers with Night Shifts

Author

Deng, Saiyue, primary, Wang, Quan, additional, Fan, Jingjing, additional, Yang, Xiaoyun, additional, Mei, Junhua, additional, Lu, Jiajia, additional, Chen, Guohua, additional, Yang, Yuan, additional, Liu, Wenhua, additional, Wang, Runsen, additional, Han, Yujia, additional, Sheng, Rong, additional, Wang, Wei, additional, Ba, Li, additional, and Ding, Fengfei, additional

Published
2022
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8. Correlation of Circadian Rhythms of Heart Rate Variability Indices with Stress, Mood, and Sleep Status in Female Medical Workers with Night Shifts

Author

Deng,Saiyue, Wang,Quan, Fan,Jingjing, Yang,Xiaoyun, Mei,Junhua, Lu,Jiajia, Chen,Guohua, Yang,Yuan, Liu,Wenhua, Wang,Runsen, Han,Yujia, Sheng,Rong, Wang,Wei, Ba,Li, Ding,Fengfei, Deng,Saiyue, Wang,Quan, Fan,Jingjing, Yang,Xiaoyun, Mei,Junhua, Lu,Jiajia, Chen,Guohua, Yang,Yuan, Liu,Wenhua, Wang,Runsen, Han,Yujia, Sheng,Rong, Wang,Wei, Ba,Li, and Ding,Fengfei

Abstract

Saiyue Deng,1 Quan Wang,2 Jingjing Fan,3 Xiaoyun Yang,3 Junhua Mei,4 Jiajia Lu,5 Guohua Chen,4 Yuan Yang,1 Wenhua Liu,6 Runsen Wang,7 Yujia Han,7 Rong Sheng,7 Wei Wang,1 Li Ba,1 Fengfei Ding1,8 1Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China; 2School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, People’s Republic of China; 3Cardiac Unit, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China; 4Department of Neurology, Wuhan No.1 Hospital, Wuhan, 430022, People’s Republic of China; 5Cardiac Unit, Wuhan No.1 Hospital, Wuhan, 430022, People’s Republic of China; 6Department of Clinical Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, People’s Republic of China; 7Huawei Technologies Co, Shenzhen, People’s Republic of China; 8Department of Pharmacology, Shanghai Medical College, Fudan University, Shanghai, 200032, People’s Republic of ChinaCorrespondence: Fengfei Ding, Department of Pharmacology, Shanghai Medical College, Fudan University, Shanghai, 200032, People’s Republic of China, Tel +86 134 7625 5813, Email fengfei_ding@fudan.edu.cn Li Ba, Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China, Tel +86 136 0764 1809, Email bali2015@126.comPurpose: Heart rate variability (HRV) indices have been used as stress indicators. Rare studies investigated the associations of circadian rhythms of the HRV indices with the stress, mood, and sleep conditions in populations under stress.Methods: In total 257 female participants (203 shift workers and 54 non-shift workers) were include

Published
2022

12. Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.

Author

Fan J, Mei J, Yang Y, Lu J, Wang Q, Yang X, Chen G, Wang R, Han Y, Sheng R, Wang W, and Ding F

Subjects
Humans, Male, Adult, Female, Stress, Psychological physiopathology, Middle Aged, Photoplethysmography methods, Photoplethysmography instrumentation, Electrocardiography, Ambulatory instrumentation, Electrocardiography, Ambulatory methods, Sleep physiology, Accelerometry instrumentation, Heart Rate physiology, Machine Learning
Abstract

We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24-h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non-cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart-device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning-based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication., (© 2024 John Wiley & Sons Ltd.)

Published
2024
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