Fatigue Estimation Using Peak Features from PPG Signals

Yi-Xiang Chen, Chin-Kun Tseng, Jung-Tsung Kuo, Chien-Jen Wang, Shu-Hung Chao, Lih-Jen Kau, Yuh-Shyan Hwang and Chun-Ling Lin ()
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Yi-Xiang Chen: Department of Electrical Engineering, Ming Chi University of Technology, No. 84, Gongzhuan Rd., Taishan Dist., New Taipei City 243, Taiwan
Chin-Kun Tseng: Department of Electronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan
Jung-Tsung Kuo: Department of Electronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan
Chien-Jen Wang: Department of Electronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan
Shu-Hung Chao: Department of Electronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan
Lih-Jen Kau: Department of Electronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan
Yuh-Shyan Hwang: Department of Electronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan
Chun-Ling Lin: Department of Electrical Engineering, Ming Chi University of Technology, No. 84, Gongzhuan Rd., Taishan Dist., New Taipei City 243, Taiwan

Mathematics, 2023, vol. 11, issue 16, 1-23

Abstract: Fatigue is a prevalent subjective sensation, affecting both office workers and a significant global population. In Taiwan alone, over 2.6 million individuals—around 30% of office workers—experience chronic fatigue. However, fatigue transcends workplaces, impacting people worldwide and potentially leading to health issues and accidents. Gaining insight into one’s fatigue status over time empowers effective management and risk reduction associated with other ailments. Utilizing photoplethysmography (PPG) signals brings advantages due to their easy acquisition and physiological insights. This study crafts a specialized preprocessing and peak detection methodology for PPG signals. A novel fatigue index stems from PPG signals, focusing on the dicrotic peak’s position. This index replaces subjective data from the brief fatigue index (BFI)-Taiwan questionnaire and heart rate variability (HRV) indices derived from PPG signals for assessing fatigue levels. Correlation analysis, involving sixteen healthy adults, highlights a robust correlation (R > 0.53) between the new fatigue index and specific BFI questions, gauging subjective fatigue over the last 24 h. Drawing from these insights, the study computes an average of the identified questions to formulate the evaluated fatigue score, utilizing the newfound fatigue index. The implementation of linear regression establishes a robust fatigue assessment system. The results reveal an impressive 91% correlation coefficient between projected fatigue levels and subjective fatigue experiences. This underscores the remarkable accuracy of the proposed fatigue prediction in evaluating subjective fatigue. This study further operationalized the proposed PPG processing, peak detection method, and fatigue index using C# in a computer environment alongside a PPG device, thereby offering real-time fatigue indices to users. Timely reminders are employed to prompt users to take notice when their index exceeds a predefined threshold, fostering greater attention to their physical well-being.

Keywords: fatigue; photoplethysmography (PPG); heart rate variability (HRV); brief fatigue index (BFI)-Taiwan form (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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