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Open AccessArticle

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

1
Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
2
Scientific Clinical Institutes ICS Maugeri, 27100 Pavia, Italy
3
Department of Chemical, Materials and Production Engineering, University of Naples Federico II, 80125 Naples, Italy
4
Department of Information Technologies and Electrical Engineering, University of Naples Federico II, 80125 Naples, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Mario Munoz-Organero and Nicola Lopomo
Sensors 2021, 21(8), 2593; https://doi.org/10.3390/s21082593
Received: 17 February 2021 / Revised: 2 April 2021 / Accepted: 5 April 2021 / Published: 7 April 2021
(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)
Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity. View Full-Text
Keywords: biomechanical risk assessment; ergonomics; feature extraction; health monitoring; IMUs; lifting; machine learning; NIOSH; wearable device; work-related musculoskeletal disorders biomechanical risk assessment; ergonomics; feature extraction; health monitoring; IMUs; lifting; machine learning; NIOSH; wearable device; work-related musculoskeletal disorders
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MDPI and ACS Style

Donisi, L.; Cesarelli, G.; Coccia, A.; Panigazzi, M.; Capodaglio, E.M.; D’Addio, G. Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning. Sensors 2021, 21, 2593. https://doi.org/10.3390/s21082593

AMA Style

Donisi L, Cesarelli G, Coccia A, Panigazzi M, Capodaglio EM, D’Addio G. Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning. Sensors. 2021; 21(8):2593. https://doi.org/10.3390/s21082593

Chicago/Turabian Style

Donisi, Leandro; Cesarelli, Giuseppe; Coccia, Armando; Panigazzi, Monica; Capodaglio, Edda M.; D’Addio, Giovanni. 2021. "Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning" Sensors 21, no. 8: 2593. https://doi.org/10.3390/s21082593

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