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

Individual evaluation of fatigue at work to enhance the safety performance in the construction industry: A systematic review

The construction industry is recognized as one of the most hazardous industries globally due to the dynamic on site activities and labour-intensive characteristics. The construction tasks are physically and cognitively demanding therefore the construction workers are prone to work fatigue which compromises safety performance. The evaluation of fit for duty, or fitness for work (FFW) aims to determine if workers are at risk of adverse impacts of ill-health, injury or accidents. This systematic review aimed to critically summarize up-to-date measures and evaluation tools that were employed to monitor work fitness or fatigue specifically among construction workers. Adhering with the PRISMA protocol, three databases were searched from the inception to 2022, with a total combination of 37 keywords, concluding to the selection of 20 relevant articles. The Mixed Method Appraisal Tool (MMAT) was used as the guide for the study appraisal. A total of 20 articles were reviewed, published from 2008-2022. Majority of the studies employed experimental design. The review identified the subjective evaluation scales and objective measurement tool. The subjective self-response questionnaires can be categorized into single dimension or multidimension covering both physical and mental fitness; whereas the objective measurement tool can be categorized into physiological metrics, physical and cognitive performance measure. The available scientific evidence has raised the relevant issues for on-site practicality and potentially guide the formulation of evidence-based guidelines for the FFW assessment in the construction industry.

Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters

Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables' contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model's functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research.

Using Arterial Pulse and Laser Doppler Analyses to Discriminate between the Cardiovascular Effects of Different Running Levels

BACKGROUND AND AIMS

Running can induce advantageous cardiovascular effects such as improved arterial stiffness and blood-supply perfusion. However, the differences between the vascular and blood-flow perfusion conditions under different levels of endurance-running performance remains unclear. The present study aimed to assess the vascular and blood-flow perfusion conditions among 3 groups (44 male volunteers) according to the time taken to run 3 km: Level 1, Level 2, and Level 3.

METHODS

The radial blood pressure waveform (BPW), finger photoplethygraphy (PPG), and skin-surface laser-Doppler flowmetry (LDF) signals of the subjects were measured. Frequency-domain analysis was applied to BPW and PPG signals; time- and frequency-domain analyses were applied to LDF signals.

RESULTS

Pulse waveform and LDF indices differed significantly among the three groups. These could be used to evaluate the advantageous cardiovascular effects provided by long-term endurance-running training, such as vessel relaxation (pulse waveform indices), improvement in blood supply perfusion (LDF indices), and changes in cardiovascular regulation activities (pulse and LDF variability indices). Using the relative changes in pulse-effect indices, we achieved almost perfect discrimination between Level 3 and Level 2 (AUC = 0.878). Furthermore, the present pulse waveform analysis could also be used to discriminate between the Level-1 and Level-2 groups.

CONCLUSIONS

The present findings contribute to the development of a noninvasive, easy-to-use, and objective evaluation technique for the cardiovascular benefits of prolonged endurance-running training.

A Classification Method for Workers' Physical Risk

In Industry 4.0 scenarios, wearable sensing allows the development of monitoring solutions for workers' risk prevention. Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker's complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy 88.7±7.3% for the model trained with max(HR), std(RR) and std(HR)).

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

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.

Assessing the Impact of Work Activities on the Physiological Load in a Sample of Loggers in Sicily (Italy)

Occupational logging activities expose workers to a wide range of risk factors, such as lifting heavy loads, prolonged, awkward positioning of the lower back, repetitive movements, and insufficient work pauses. Body posture has an important impact on the level of physiological load. The present study involved a group of 40 loggers in the province of Enna (Sicily, Italy) with the aim of defining the impact of logging activities on the workers’ physiological strain during the three primary work tasks of felling, delimbing, and bucking. The Zephyr Bioharness measurement system was used to record trunk posture and heart rate data during work tasks. The NASA TLX questionnaire was used to explore workers’ effort perception of the work tasks. Based on our results, the most demanding work task was tree felling, which requires a higher level of cardiac cost and longer periods spent in awkward trunk postures. The perceived physiological workload was consistently underestimated, especially by the more experienced loggers. Lastly, as the weight of the chainsaw increased, the cardiac load increased.