International Journal of Science, Engineering and TechnologyAuthors: Mr. Sahil A. Bodke, Ms. Devika D. More, Ms. Samruddhi M. Pansare, Prof. P. A. Mande, Prof. A.P.Bangar, Prof. S. B. Bhosale
Abstract: Workers in industrial workplaces are still exposed to toxic gases and thermal stresses, are victims of mechanical injuries and have been.fatigue-related accidents. Conventional safety systems have remained reactive until now and have responded after an incident takes place. The emergence of Artificial Intelligence (AI), the Internet of Things (IoT), and cutting-edge wearable sensor technology is currently giving rise to new opportunities for proactive occupational safety. This paper presents a Smart Industrial Safety Wearable System (SISWS) whose performance is validated after prototype testing of around 1200 sensors observations under six hazards. The system shows 78% accuracy in hazard detection, 94% in PPE detection, 92% reliability in sensor performance, and can generate alerts in less than 3 seconds, contributing to a reduction of emergency response by 60%. The model will model safety conditions’ classification and predict risk using a hybrid Decision Tree and Long Short-Term Memory (LSTM). The selection of the model over Random Forest and pure CNN was driven by its aptness for edge deployment and its ability to identify temporal patterns in sequential sensor streams. The key research gaps identified in fatigue prediction in an industrial environment are: 1. Lack of multi-modal sensor fusion with real-time edge AI; 2. Insufficient datasets for industrial fatigue prediction; 3. Limited ergonomic wearables for a tough industrial environment; and 4. Lack of XAI in safety-critical decision-making. This study sets a solid base for further advancement involving AI to create an occupational safety system with a prevention focus.
DOI: http://doi.org/10.5281/zenodo.20233475
