A Design of Experiments for Statistically Predicting Risk of Adverse Health Effects on Drivers Exposed to Vertical Vibrations

Since patient comfort during transport is a matter of paramount importance, this paper aims to determine the possibilities of applying neural networks for its prediction and monitoring. Specific objectives of the research include monitoring and predicting patient transport comfort, with subjective assessment of comfort by medical personnel. An original Android application that collects signals from an accelerometer and a GPS sensor was used with the aim of achieving the research goals. The collected signals were processed and a total of twelve parameters were calculated. A multilayer perceptron was created in the proposed research. The evaluation results indicate acceptable accuracy and give the possibility to apply the same model to the next patient transport. The root mean square error was 0.0215 and the overall confusion matrix prediction accuracy was 90.07%. Moreover, the results were validated in real usage. The limitations and future work are highlighted.

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“…A strong association between these was shown in [26]. The authors of [27] concluded that both the age and the weight of drivers were important.…”

Section: Related Workmentioning

confidence: 96%

Patient comfort level prediction during transport using artificial neural network

Jovanovic¹,

Blagojević²,

Janković³

et al. 2019

Turk J Elec Eng & Comp Sci

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“…Several different risk factors associated with causing work-related MSK disorders are associated with occupational driving [ 11 , 25 , 26 , 27 , 28 , 29 ]. For example, prolonged hours spent in a seated position while driving is associated with causing knee pain [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Musculoskeletal Disorders Associated with Occupational Driving: A Systematic Review Spanning 2006–2021

Pickard

Burton

Yamada

et al. 2022

IJERPH

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Several occupations require workers to spend long periods of time driving road vehicles. This occupational task is associated with musculoskeletal disorders. The purpose of this review was to collate, synthesize, and analyze research reporting on musculoskeletal disorders associated with occupational driving, in order to develop a volume of evidence to inform occupational disorder mitigation strategies. A systematic search of academic databases (PubMed, EBSCO host, CINAHL, SPORTDiscus, and Web of Science) was performed using key search terms. Eligible studies were critically appraised using the Joanna Briggs Institute critical appraisal checklists. A Cohen’s kappa analysis was used to determine interrater agreement between appraisers. Of the 18,254 identified studies, 25 studies were selected and appraised. The mean critical appraisal score is 69% (range 38–100%), with a fair level of agreement (k = 0.332). The studies report that musculoskeletal disorders, most commonly lower back pain, is of concern in this population, particularly in truck, bus, and taxi drivers. Risk factors for these occupations include long hours in a sitting position, years in the profession, vehicle ergonomics, and vibration.

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“…Long-term WBVs can generate adverse health hazards for the lumbar spine. However, they are generally sufficiently powerful to establish a dose-effect relationship between exposure to WBV and the risk of lumbar disorders [15]. As is the case of all other objects in nature, the organs of the human body also emit natural frequency vibrations.…”

Section: Introductionmentioning

confidence: 99%

Pavement performance levels causing human health risks

2018

JCE

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Pavement performance levels causing human health risks This study investigates performance thresholds of hot mix asphalt pavements in terms of health risks from vibrations affecting drivers of passenger cars on urban roads. The whole-body vibration values in vertical direction are measured at various riding speeds at some pavement sections that are on the pavement condition index (PCI) rating scale proposed by the PAVER system, while the vibration values (VDVz) are calculated. Evaluation results reveal that pavement performance levels have to be at least "fair" (PCI scale range between 55 and 70) to ensure that the health of human body is not negatively affected at 50 km/h riding speed during a daily 8-hour driving.

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A Design of Experiments for Statistically Predicting Risk of Adverse Health Effects on Drivers Exposed to Vertical Vibrations

Abstract: An injury risk factor (IRF),adverse health effect whole-body vibration spinal loading finite-element analysis ageing design of experiments.

Cited by 7 publications

References 25 publications

Patient comfort level prediction during transport using artificial neural network

Patient comfort level prediction during transport using artificial neural network

Musculoskeletal Disorders Associated with Occupational Driving: A Systematic Review Spanning 2006–2021

Pavement performance levels causing human health risks

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