Pages 274–279
Abstract
Research summary supplied by the authors
Occupational fatigue in industrial workers is still a major problem, leading to loss of productivity, risk of accidents and long-term musculoskeletal disorders. Conventional methods for fatigue measurement are based on subjective reports or laboratory equipment and are not suitable for real-time applications in the field. We propose the development of a Smart Footwear with pressure-sensing insoles (customized) that incorporates intelligent analytics, which can autonomously monitor for fatigue continuously during work-days in industrial environments. The specific aim is to develop an AI-based fatigue-prediction model with gait changes, Center of Pressure (COP) and plantar pressure distribution patterns, design a preventive safety shoe intervention that can generate early warning. The approach involves the fabrication of wearable flexible pressure sensor array based on conductive fiber to implement a safety shoe with wireless communication (BLE), and a real-time data pipeline for fatigue prediction using machine learning. Fieldassessment prior model fabrication and field testing with industrial workers will be assisted by subjective fatigue assessment scales and bio-mechanical validations. Anticipated outcomes will be the determination of fatigue-specific gait biomarkers, the creation of predictive algorithms for early detection of fatigue, and a hardware prototype for real-time ergonomic risk feedback. This research is valuable for us by shortening the period taken to move from traditional shoes to technology-supported smart safety shoes providing additional functions, thus improving global competitiveness. The proposed solution supports worker wellbeing, reduces occupational hazards, and opens new opportunities for Industry-aligned smart PPE innovation and commercialization within the Indian leather and safety-footwear market.