Assessing occupational risk of heat stress at construction: A worker-centric wearable sensor-based approach

Shakerian, Shahrad; Habibnezhad, Mahmoud; Ojha, Amit; Lee, Gaang; Liu, Yizhi; Jebelli, Houtan; Lee, Sang Hyun

doi:10.1016/j.ssci.2021.105395

Cited by 54 publications

(29 citation statements)

References 80 publications

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“…Pattern recognition and parametric tests were used to identify stress hot spots on this walk, and 4 machine learning models were trained to test the predictive power of the immediate environment. Another study used machine learning models for heat strain assessment [ 79 ].…”

Section: Resultsmentioning

confidence: 99%

“…Three studies (3/14, 21%) [ 46 , 64 , 78 ] compared different methods of using wearables data for core temperature estimation to measured values and found good overall results using HR [ 78 ]; HR and skin temperature [ 46 ]; and HR, skin temperature, and near-body temperature [ 64 ]. Four studies [ 43 , 52 , 67 , 79 ] evaluated different methods to use wearables data (HR or cardiac cost, electrodermal activity, and skin temperature) to assess heat strain and all found high sensitivity or accuracy. Two of these studies paired wearables data with skin temperature or rectal and core temperature measurements for assessment.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Wearables for Measuring Health Effects of Climate Change–Induced Weather Extremes: Scoping Review

Koch¹,

Matzke²,

Huhn³

et al. 2022

JMIR Mhealth Uhealth

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Background Although climate change is one of the biggest global health threats, individual-level and short-term data on direct exposure and health impacts are still scarce. Wearable electronic devices (wearables) present a potential solution to this research gap. Wearables have become widely accepted in various areas of health research for ecological momentary assessment, and some studies have used wearables in the field of climate change and health. However, these studies vary in study design, demographics, and outcome variables, and existing research has not been mapped. Objective In this review, we aimed to map existing research on wearables used to detect direct health impacts and individual exposure during climate change–induced weather extremes, such as heat waves or wildfires. Methods We conducted a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework and systematically searched 6 databases (PubMed [MEDLINE], IEEE Xplore, CINAHL [EBSCOhost], WoS, Scopus, Ovid [MEDLINE], and Google Scholar). The search yielded 1871 results. Abstracts and full texts were screened by 2 reviewers (MK and IM) independently using the inclusion and exclusion criteria. The inclusion criteria comprised studies published since 2010 that used off-the-shelf wearables that were neither invasive nor obtrusive to the user in the setting of climate change–related weather extremes. Data were charted using a structured form, and the study outcomes were narratively synthesized. Results The review included 55,284 study participants using wearables in 53 studies. Most studies were conducted in upper–middle-income and high-income countries (50/53, 94%) in urban environments (25/53, 47%) or in a climatic chamber (19/53, 36%) and assessed the health effects of heat exposure (52/53, 98%). The majority reported adverse health effects of heat exposure on sleep, physical activity, and heart rate. The remaining studies assessed occupational heat stress or compared individual- and area-level heat exposure. In total, 26% (14/53) of studies determined that all examined wearables were valid and reliable for measuring health parameters during heat exposure when compared with standard methods. Conclusions Wearables have been used successfully in large-scale research to measure the health implications of climate change–related weather extremes. More research is needed in low-income countries and vulnerable populations with pre-existing conditions. In addition, further research could focus on the health impacts of other climate change–related conditions and the effectiveness of adaptation measures at the individual level to such weather extremes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Wearables for Measuring Health Effects of Climate Change–Induced Weather Extremes: Scoping Review

Koch¹,

Matzke²,

Huhn³

et al. 2022

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…The resulting dataset, comprising a total of 750 sets of 21 features (six from heart rate, four from breathing rate, four from core temperature and seven from personal characteristics) together with the corresponding fatigue levels, was normalised and fed to machine learning algorithms. Various supervised classification algorithms, previously used for modelling physiological variables responses [ 12 , 15 , 18 , 34 , 35 ] and many health-related purposes [ 36 , 37 ], were evaluated since no previous study assessed this combination of variables for firefighting applications. The tested algorithms included K-nearest neighbours, Boosted Trees (Gradient-boosted Trees, XGBoosted Trees and RUSBoosted Trees), Bagged Trees, Random Forests, Support Vector Machines with different kernel functions (linear, quadratic, cubic and Gaussian) and Artificial Neural Networks.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

Bustos

Cardoso

Rios

et al. 2022

Sensors

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Physical fatigue is a serious threat to the health and safety of firefighters. Its effects include decreased cognitive abilities and a heightened risk of accidents. Subjective scales and, recently, on-body sensors have been used to monitor physical fatigue among firefighters and safety-sensitive professionals. Considering the capabilities (e.g., noninvasiveness and continuous monitoring) and limitations (e.g., assessed fatiguing tasks and models validation procedures) of current approaches, this study aimed to develop a physical fatigue prediction model combining cardiorespiratory and thermoregulatory measures and machine learning algorithms within a firefighters’ sample. Sensory data from heart rate, breathing rate and core temperature were recorded from 24 participants during an incremental running protocol. Various supervised machine learning algorithms were examined using 21 features extracted from the physiological variables and participants’ characteristics to estimate four physical fatigue conditions: low, moderate, heavy and severe. Results showed that the XGBoosted Trees algorithm achieved the best outcomes with an average accuracy of 82% and accuracies of 93% and 86% for recognising the low and severe levels. Furthermore, this study evaluated different methods to assess the models’ performance, concluding that the group cross-validation method presents the most practical results. Overall, this study highlights the advantages of using multiple physiological measures for enhancing physical fatigue modelling. It proposes a promising health and safety management tool and lays the foundation for future studies in field conditions.

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“…Several factors, including intense physical activity, personal protective clothing, and frequent heat events on construction sites, put construction workers at high risk of excessive heat exposure. As such, many studies have used wearable sensors to capture various physiological signals related to heat stress exposure in construction workers (68)(69)(70). They concluded that the use of wearable sensors J o u r n a l P r e -p r o o f allowed workers to make health decisions based on objective information and warnings, and thus opened up new avenues for disease prevention and monitoring.…”

Section: The Application Of Wearable Sensors For Assessing Workers' H...mentioning

confidence: 99%

Test-retest reliability, validity, and responsiveness of a textile-based wearable sensor for real-time assessment of physical fatigue in construction bar-benders

Anwer

Antwi‐Afari

et al. 2021

Journal of Building Engineering

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Assessing occupational risk of heat stress at construction: A worker-centric wearable sensor-based approach

Cited by 54 publications

References 80 publications

Wearables for Measuring Health Effects of Climate Change–Induced Weather Extremes: Scoping Review

Wearables for Measuring Health Effects of Climate Change–Induced Weather Extremes: Scoping Review

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

Test-retest reliability, validity, and responsiveness of a textile-based wearable sensor for real-time assessment of physical fatigue in construction bar-benders

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