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

Leandro Donisi,Armando Coccia,Monica Panigazzi,Giovanni D’Addio,Edda Maria Capodaglio,Giuseppe Cesarelli

doi:10.3390/s21082593

Abstract

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.

Highlights

We considered two datasets: the first dataset is composed of 60 instances, 30 for each class (NO RISK, RISK), and 12 features extracted from the acceleration signals; the second dataset is composed of 60 instances, 30 for each class (NO RISK, RISK), and 12
We performed the machine learning (ML) analysis by averaging the results among the seven subjects, and we further showed the standard deviation in order to include prediction uncertainty
The results showed that the proposed combinations of features—extracted from acceleration and angular velocity signals acquired by a single inertial measurement units (IMUs) placed at pelvis—

Summary

Introduction

In addition to the more traditional quantitative or semiquantitative observational methods [7,8,9,10,11,12,13], occupational ergonomics studies in the field can employ instrumental methods that offer greater agility, precision and duration of measurement. Among the direct measurement methods, wearable inertial systems based on inertial measurement units (IMUs) play an important role in the biomechanical risk assessment [14], and they look very promising for occupational medicine and ergonomics applications [15]. In the field of risk assessment, wearable inertial technology represents a significant advance in comparison to the evaluation tools traditionally used in ergonomics [16], especially regarding the degree of precision and possibility of automatic measurement detection

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Discussion

Conclusion