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

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 8; p. 2593
• Main Authors: Donisi, Leandro, Cesarelli, Giuseppe, Coccia, Armando, Panigazzi, Monica, Capodaglio, Edda Maria, D’Addio, Giovanni
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.04.2021; MDPI
• Subjects: Algorithms; Biomechanical Phenomena; biomechanical risk assessment; Biomechanics; Ergonomics; feature extraction; health monitoring; Humans; IMUs; Laboratories; Lifting; Machine Learning; Methods; National Institute for Occupational Safety and Health, U.S; Risk Assessment; Sensors; United States; Velocity; Wearable Electronic Devices; United States; IMUs; wearable device; NIOSH; health monitoring; feature extraction; ergonomics; lifting; work-related musculoskeletal disorders; machine learning; biomechanical risk assessment
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21082593

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.