A data-driven approach to modeling physical fatigue in the workplace using wearable sensors

Maman, Zahra Sedighi; Yazdi, Mohammad Ali Alamdar; Cavuoto, Lora; Megahed, Fadel M.

doi:10.1016/j.apergo.2017.02.001

Cited by 182 publications

(104 citation statements)

References 66 publications

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“…In our research, the physiological data collected by Maman et al [22] were used to detect workers' physical fatigue. They used five different sensors' features in their research.…”

Section: Discussionmentioning

confidence: 99%

“…RPE [27] was used to label the data. Fatigue occurrence was detected using RPE analysis and a binary decision rule as follows [22]:…”

Section: Physically Fatiguing Tasksmentioning

confidence: 99%

“…In [22], participants did three experimental sessions. In each session, one physically fatiguing task was performed for three hours divided into one-hour periods.…”

Section: Physically Fatiguing Tasksmentioning

confidence: 99%

“…Scientific literature confirmed that repeated lifting and bending generate high probabilities of disorders of the lower back. • Manual Material Handling (MMH): Bodily fatigue is generally caused by continuous walking, as reported by 45% of the workers of the industry [22]. The whole task involved selecting packages with various weights, viz.…”

Section: Physically Fatiguing Tasksmentioning

confidence: 99%

“…Fatigue can occur due to excessive physical workload The data used in this research were collected earlier by Maman et al using a wearable sensor (Polar CR800X). Their protocol consisted of three physically demanding tasks [22]. Five males and 3 females of different ages ranging from 18 to 62 years from the local community were engaged for a duration of 3.5 months.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Physical Fatigue Detection Using Entropy Analysis of Heart Rate Signals

et al. 2020

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Physical fatigue is one of the most important and highly prevalent occupational hazards in different industries. This research adopts a new analytical framework to detect workers’ physical fatigue using heart rate measurements. First, desired features are extracted from the heart signals using different entropies and statistical measures. Then, a feature selection method is used to rank features according to their role in classification. Finally, using some of the frequently used classification algorithms, physical fatigue is detected. The experimental results show that the proposed method has excellent performance in recognizing the physical fatigue. The achieved accuracy, sensitivity, and specificity rates for fatigue detection are 90.36%, 82.26%, and 96.2%, respectively. The proposed method provides an efficient tool for accurate and real-time monitoring of physical fatigue and aids to enhance workers’ safety and prevent accidents. It can be useful to develop warning systems against high levels of physical fatigue and design better resting times to improve workers’ safety. This research ultimately aids to improve social sustainability through minimizing work accidents and injuries arising from fatigue.

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“…In our research, the physiological data collected by Maman et al [22] were used to detect workers' physical fatigue. They used five different sensors' features in their research.…”

Section: Discussionmentioning

confidence: 99%

“…RPE [27] was used to label the data. Fatigue occurrence was detected using RPE analysis and a binary decision rule as follows [22]:…”

Section: Physically Fatiguing Tasksmentioning

confidence: 99%

“…In [22], participants did three experimental sessions. In each session, one physically fatiguing task was performed for three hours divided into one-hour periods.…”

Section: Physically Fatiguing Tasksmentioning

confidence: 99%

Section: Physically Fatiguing Tasksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Physical Fatigue Detection Using Entropy Analysis of Heart Rate Signals

et al. 2020

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Managing Low‐back Disorder Risk in the Workplace

Marras

Karwowski

2021

Handbook of Human Factors and Ergonomics

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Ethical Considerations of Wearable Technologies in Human Research

Gao

2021

Adv Healthcare Materials

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Wearable technologies hold great promise for disease diagnosis and patient care. Despite the flourishing research activities in this field, only a handful of wearable devices are commercialized and cleared for medical usage. The successful translation of current proof‐of‐concept prototypes requires extensive in‐human testing. There is a lag between current standards and operation protocols to guide the responsible and ethical conduct of researchers in such in‐human studies and the rapid development of the field. This essay presents relevant ethical concerns in early‐stage human research from a researcher's perspective.

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A data-driven approach to modeling physical fatigue in the workplace using wearable sensors

Cited by 182 publications

References 66 publications

Physical Fatigue Detection Using Entropy Analysis of Heart Rate Signals

Physical Fatigue Detection Using Entropy Analysis of Heart Rate Signals

Managing Low‐back Disorder Risk in the Workplace

Ethical Considerations of Wearable Technologies in Human Research

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