Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning

Biosensors, Journal Year: 2025, Volume and Issue: 15(2), P. 84 - 84

Published: Feb. 1, 2025

Repetitive lifting tasks in occupational settings often result shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these remains a significant challenge ergonomics, particularly within manufacturing environments. Traditional assessment methods frequently rely on subjective reports limited observations, which can introduce bias yield incomplete evaluations. This study addresses limitations by generating utilizing comprehensive dataset containing detailed time-series electromyography (EMG) data from 25 participants. Using high-precision wearable sensors, EMG were collected eight muscles as participants performed repetitive tasks. For each task, index was calculated using revised National Institute for Occupational Safety Health (NIOSH) equation (RNLE). Participants completed cycles low-risk high-risk four-minute period, allowing muscle performance under realistic working conditions. extensive dataset, comprising over 7 million points sampled at approximately 1259 Hz, leveraged to develop deep learning models classify risk. To provide actionable insights practical ergonomics assessments, statistical features extracted raw data. Three models, Convolutional Neural Networks (CNNs), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), employed analyze predict level. The CNN model achieved highest performance, with precision 98.92% recall 98.57%, proving its effectiveness real-time assessments. These findings underscore importance aligning architectures characteristics optimize management. By integrating sensors this enables precise, real-time, dynamic significantly enhancing workplace safety protocols. approach has potential improve planning reduce incidence severity work-related musculoskeletal disorders, ultimately promoting better outcomes across various settings.

Language: Английский

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Binary Risk vs No-Risk Classification of Load Lifting Activities Using Features Extracted from sEMG Trapezius Muscle

IFMBE proceedings, Journal Year: 2024, Volume and Issue: unknown, P. 283 - 291

Published: Jan. 1, 2024

Language: Английский

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Feasibility of Tree-Based Machine Learning Models to Discriminate Safe and Unsafe Posture During Weight Lifting

2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), Journal Year: 2023, Volume and Issue: unknown, P. 870 - 875

Published: Oct. 25, 2023

The weight lifting is defined as any activity requiring the use of human force to lift or move a load which can be potentially harmful onsetting work-related musculoskeletal disorders. purpose this study was explore feasibility four tree-based Machine Learning (ML) models - fed with time-domain features extracted from signals acquired by means one inertial measurement unit (IMU) classify safe and unsafe postures during lifting. Inertial -linear acceleration angular velocity sternum 4 healthy subjects were registered using Mobility Lab System. manually segmented in order extract for each region interest, corresponding lifting, several features. Four predictive namely Decision Tree, Random Forest, Rotation Forest AdaBoost Tree implemented their performances tested. Interesting results terms evaluation metrics binary safe/unsafe posture classification obtained accuracy values greater than 93%. In conclusion present indicated that ML specific able discriminate only IMU placed on sternum. Future investigation larger cohort could confirm potential proposed methodology.

Language: Английский

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The Impact of New Technologies on Occupational Safety and Health from the Point of View of Their Academic Interest

Springer Proceedings in Materials, Journal Year: 2024, Volume and Issue: unknown, P. 381 - 391

Published: Jan. 1, 2024

Language: Английский

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