sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

Abstract

Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology—based on wearable sensors and artificial intelligence—to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.

Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors

“…Fridolfsson et al [ 28 ] studied the feasibility of ML models, which were fed with features extracted from acceleration signals using a shoe-based sensors, to classify work-specific activities; RaF was the best algorithm, once again reaching an accuracy of up to 71%. Mudiyanselage et al [ 29 ] analyzed the level detection of risk of harmful lifting activities characterized by the Revised NIOSH Lifting Equations using ML and DL algorithms fed with features extracted from thoracic and multifidus sEMG signals, while Donisi et al [ 30 ] studied the feasibility of ML algorithms fed with frequency-domain features extracted from sEMG signals of erector spinae and multifidus muscles to discriminate the biomechanical risk associated with manual material liftings, highlighting that the best algorithm was SVM, with an accuracy equal to 96.1%.…”