Virtual Booth

Muscle fatigue remains a critical yet often overlooked hazard in labour-intensive industries such as construction and manufacturing, significantly contributing to work-related musculoskeletal disorders (WMSDs), reduced productivity, and increased accident rates. Conventional fatigue monitoring methods, which rely on subjective assessment and manual observation, are largely reactive and ineffective in preventing injuries before they occur. This study introduces A.R.M.O.R. (Analytic Real-time Muscle Operational Risk-sensor), an innovative wearable system designed to enable proactive, real-time monitoring of muscle fatigue and operational risk.

The proposed system integrates surface electromyography (sEMG) sensors and contact-based temperature sensors into a standard high-visibility safety vest, allowing continuous monitoring of muscle activity and metabolic heat. A key contribution of this work is the development of a Hybrid Fatigue Index (HFI), which employs a multi-sensor fusion approach to combine muscle electrical activity, thermal response, body mass index (BMI), and environmental temperature into a unified fatigue risk score. This integration significantly reduces false alarms commonly associated with single-sensor systems.

The system is powered by an ESP32 microcontroller that performs edge computing, enabling real-time data processing and immediate risk assessment. When a critical fatigue threshold is reached, the system activates an on-body auditory alert while simultaneously transmitting data to an IoT-based dashboard for remote monitoring by safety personnel. Experimental validation demonstrates that the system can reliably detect transitions into high-risk fatigue states, typically within 25 minutes of sustained heavy activity, allowing early intervention before physical failure occurs.

A.R.M.O.R. offers strong commercial potential as a scalable and cost-effective solution for both SMEs and large industries. This innovation contributes to enhancing workplace safety, reducing injury-related costs, and aligns with Sustainable Development Goals (SDG 3, SDG 8, and SDG 9), bridging the gap between laboratory research and real-world industrial applications.