Developing Affordable Tactile Gloves for Real-Time Musculoskeletal Strain Monitoring in Heavy Lifting Using Machine Learning

Preventing workplace injuries caused by musculoskeletal strain during heavy lifting is crucial, as such injuries impact both worker health and organizational efficiency. Effective monitoring tools, such as tactile gloves,  are needed to assess exertion levels and prevent overexertion. However, the high cost of commercial tactile gloves limits their accessibility in many industries, creating a need for affordable alternatives. To address this gap, this research develops a cost-effective prototype that combines force sensitive resistor tactile gloves with electromyography sensors to measure applied pressure and muscle activity in real time. The system uses bluetooth low energy technology to transmit data wirelessly, enabling continuous monitoring of fatigue and exertion levels without hindering worker mobility. This prototype will be tested in simulated lifting and carrying tasks to gather data on pressure distribution and muscle activation patterns, which will then be processed through a machine learning model to detect patterns indicative of injury risks. The machine learning component enables real-time assessment and supports predictive insights that can prompt timely ergonomic adjustments and interventions to enhance safety. By making advanced ergonomic monitoring more affordable and accessible, this research contributes a scalable tool for industries with high physical demands, such as manufacturing, logistics, and construction. Ultimately, this solution aims to advance occupational health by reducing injury risks and supporting data-driven ergonomic improvements, aligning with the broader goals of human factors and ergonomics in enhancing worker well-being and system performance.