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Feasibility analysis of heart rate monitoring of construction workers using a photoplethysmography (PPG) sensor embedded in a wristband-type activity tracker
- Feasibility analysis of heart rate monitoring of construction workers using a photoplethysmography (PPG) sensor embedded in a wristband-type activity tracker
- Hwang, Sungjoo; Seo, JoonOh; Jebelli, Houtan; Lee, SangHyun
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- AUTOMATION IN CONSTRUCTION
- AUTOMATION IN CONSTRUCTION vol. 71, pp. 372 - 381
- Construction worker; Heart rate; Occupational health; Remote sensing; Work physiology
- ELSEVIER SCIENCE BV
- SCIE; SCOPUS
- Document Type
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- With increasing concerns regarding occupational safety and health, managing excessive physical workloads of workers is critical to prevent workers' fatigue, injuries, errors, or accidents at physically demanding workplaces such as construction. In this regard, heart rate (HR) is an effective physiological indicator of workers' physical demands. Currently, off-the-shelf wearable activity trackers (e.g., wristband-type) can monitor a worker's HR with its embedded photoplethysmography (PPG) sensor. However, PPG signals can be highly affected by signal noises resulted from user's movements, and thus the exact HR extraction from a wristband-type PPG may not be sufficiently accurate during intensive construction tasks. In this paper, we investigate the accuracy of a PPG sensor embedded in a wristband-type tracker to see if it can be used for construction. Through field data collection from seven construction workers, we conduct a comparative HR analysis between a PPG sensor and an electro-cardiography (ECG) sensor in a chest strap used as ground truth. The results show that a PPG-based HR sensor in a wristband-type activity tracker has a potential for practicable HR monitoring of construction workers with 4.79% of mean-average-percentage-error (MAPE) and 0.85 of correlation coefficient for whole datasets (4.44%, 4.52%, and 533% of MAPEs and 0.89, 0.70, and 0.61 of correlation coefficients during light works with <90 bpm of HRs, moderate works with 90-110 bpm of HRs, and heavy works with >110 bpm of HRs, respectively). Because there is still room for improvement of the accuracy, particularly during heavy works, we also investigate the factors affecting the accuracy of HR monitoring using inequality statistics. From this secondary investigation, we found the major sources of error including noises from motion artifacts. With advanced noise-cancellation techniques, it is expected that that field HR monitoring using wearable activity trackers can be used to evaluate worker's physical demands from diverse construction tasks in a non-intrusive and affordable way. As a result, our work will help manage excessive workloads (e.g., flexing work/rest plans) so that a worker can sustain his/her given tasks during working time in a safer and healthier way. (C) 2016 Elsevier B.V. All rights reserved.
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