Assessment of construction workers’ perceived risk using physiological data from wearable sensors: A machine learning approach

Lee, Gaang; Choi, Byungjoo; Jebelli, Houtan; Lee, Sang Hyun

doi:10.1016/j.jobe.2021.102824

Cited by 50 publications

(36 citation statements)

References 115 publications

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“…In addition, only three investigations [40][41][42] declared randomisation procedures in the participants category, and none described power adjustment or consideration to account for the adequacy of sample size. For four cases [48,53,65,76], with samples under 10 participants, the number of subjects was not considered representative to assure statistical power in the study. As part of the data sources evaluation, nearly half of the papers [42][43][44][45][46]48,49,51,52,54,56,58,60,63,72,73,77]) indicated that not all subjects homogeneously completed all parts of the protocols.…”

Section: Risk Of Bias Assessment and Quality Of Resultsmentioning

confidence: 99%

“…These devices included the Equivital LifeMonitor [21] (a multivariable signal device with ECG and respiratory monitor, inbuilt medical-grade thermometer and tri-axis accelerometer), identified in seven studies [42,51,57,63,69,73,77], and the Zephyr status monitor (including ECG, skin temperature, breathing rate and tri-axis inertial signals) observed in three [45,53,56]. Wristband-type devices such as the E4 from Empatica (including PPG, electrodermal activity, skin temperature and tri-axis signals) and the Basis Fitness (PPG and tri-axial signals) were also used in two studies each [49,50,59,76].…”

Section: Physiological Monitoring Systems and Processing Methodsmentioning

confidence: 99%

“…For this purpose, the chosen equipment consisted mainly of Polar and Garmin heart rate monitors and Equivital, Zephyr and Ecgmove multivariable signals monitors. On the other hand, thermal responses (skin or core body temperature) were identified in 11 of the 38 publications [40,42,45,50,51,65,68,69,72,73,76]. Core temperature was measured with ingestible thermometric pills and skin temperature from various body parts was mainly collected using Equivital LifeMonitors and E4 Empatica wristband-type sensors.…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

“…Most of the articles recurred to statistical tests to examine the data and present outcomes. Less observed was the use of customised algorithms and machine learning methods [47,50,62,76].…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

“…Additionally, what was common among 11 publications [42,46,48,53,58,65,[71][72][73]76,77] was the assessment of the physiological workload resulting from specific activities that was defined in several ways depending on the monitoring goal. However, all studies agreed on how continuous monitoring of physical load in workers can increase an understanding of their health conditions and performance effects [48,53].…”

Section: Physiological Workloadmentioning

confidence: 99%

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Applicability of Physiological Monitoring Systems within Occupational Groups: A Systematic Review

Bustos

Guedes

Baptista

et al. 2021

Sensors

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The emergence of physiological monitoring technologies has produced exceptional opportunities for real-time collection and analysis of workers’ physiological information. To benefit from these safety and health prognostic opportunities, research efforts have explored the applicability of these devices to control workers’ wellbeing levels during occupational activities. A systematic review is proposed to summarise up-to-date progress in applying physiological monitoring systems for occupational groups. Adhering with the PRISMA Statement, five databases were searched from 2014 to 2021, and 12 keywords were combined, concluding with the selection of 38 articles. Sources of risk of bias were assessed regarding randomisation procedures, selective outcome reporting and generalisability of results. Assessment procedures involving non-invasive methods applied with health and safety-related goals were filtered. Working-age participants from homogeneous occupational groups were selected, with these groups primarily including firefighters and construction workers. Research objectives were mainly directed to assess heat stress and physiological workload demands. Heart rate related variables, thermal responses and motion tracking through accelerometry were the most common approaches. Overall, wearable sensors proved to be valid tools for assessing physiological status in working environments. Future research should focus on conducting sensor fusion assessments, engaging wearables in real-time evaluation methods and giving continuous feedback to workers and practitioners.

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Section: Risk Of Bias Assessment and Quality Of Resultsmentioning

confidence: 99%

Section: Physiological Monitoring Systems and Processing Methodsmentioning

confidence: 99%

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

“…Most of the articles recurred to statistical tests to examine the data and present outcomes. Less observed was the use of customised algorithms and machine learning methods [47,50,62,76].…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

Section: Physiological Workloadmentioning

confidence: 99%

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Applicability of Physiological Monitoring Systems within Occupational Groups: A Systematic Review

Bustos

Guedes

Baptista

et al. 2021

Sensors

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Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Kim,

Min,

2023

Advanced Sensor Research

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This review article explores the transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and photoplethysmogram (PPG). The integration of machine learning with these biosignals has led to remarkable breakthroughs in various medical monitoring and human–machine interface applications. For ECG, machine learning enables automated heartbeat classification and accurate disease detection, improving cardiac healthcare with early diagnosis and personalized interventions. EMG technology, combined with machine learning, facilitates real‐time prediction and classification of human motions, revolutionizing applications in sports medicine, rehabilitation, prosthetics, and virtual reality interfaces. EEG analysis powered by machine learning goes beyond traditional clinical applications, enabling brain activity understanding in psychology, neurology, and human–computer interaction, and holds promise in brain–computer interfaces. PPG, augmented with machine learning, has shown exceptional progress in diagnosing and monitoring cardiovascular and respiratory disorders, offering non‐invasive and accurate healthcare solutions. These integrated technologies, powered by machine learning, open new avenues for medical monitoring and human–machine interaction, shaping the future of healthcare.

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Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

2022

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Analysis & Sensing www.analysis-sensing.org Review doi.org/10.1002/anse.202200062Wearable devices have received significant attention recently for their ability to monitor critical physiological signals noninvasively, such as electrocardiography, electroencephalography, electromyography, and photoplethysmography. These biointegrated wearable systems can potentially fill gaps in conventional clinical practice by providing highly cost-effective health characterization and portable continuous health monitoring. Further, the physiological signals measured by wearables require post-processing to derive meaningful values, such as heart rate or blood oxygen saturation. This requirement, in conjunction with the smaller form factor and limited sensor count of the miniaturized systems, often necessitates robust signal processing and data analysis to approach the stringent performance specifications of conventional medical devices, and machine learning techniques have found success in filling this analytical role for their ability to learn complex functional relationships. Thus, this review outlines a systematic summary of the latest research on various wearable devices and their biosignal sensing and signal processing methods, emphasizing machine learning. We also discuss the developmental challenges and advantages of current machine-learning methods, while suggesting research directions for future studies.

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Assessment of construction workers’ perceived risk using physiological data from wearable sensors: A machine learning approach

Cited by 50 publications

References 115 publications

Applicability of Physiological Monitoring Systems within Occupational Groups: A Systematic Review

Applicability of Physiological Monitoring Systems within Occupational Groups: A Systematic Review

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

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