IIIT Hyderabad develops wearable system to enable rapid response in industrial accidents
Hyderabad, April 14, 2026 – A team at the International Institute of Information Technology (IIIT) Hyderabad has unveiled a groundbreaking smart wearable safety system designed to detect workplace accidents in real time and alert supervisors within seconds. This innovative technology addresses critical delays in emergency response, particularly in high-risk industrial environments.
Overview of the System
The wearable safety system was developed at IIIT Hyderabad’s Centre for VLSI and Embedded Systems Technology, led by Abhishek Srivastava. The primary focus of this technology is on sectors such as thermal power plants, oil refineries, and construction sites, where workers often operate in dispersed and hazardous conditions.
Real-Time Alerts to Bridge Reporting Gaps
Industrial accidents frequently go unreported immediately, especially in large facilities where workers are spread across vast areas. The newly developed wearable device aims to close this reporting gap by automatically flagging incidents without depending on manual reporting. This feature is crucial in ensuring timely responses to accidents.
Device Design and Functionality
The device is designed as a compact belt-mounted unit that connects to a base station and a central monitoring system. It performs several key functions:
- Tracks worker location
- Detects hazardous gases
- Monitors vital signs
- Identifies falls
In the event of an accident, the device sends an alert within seconds, prompting the central monitoring station to trigger an audible alarm. This rapid response mechanism is vital, as it aligns with the concept of the “golden hour”—the first 60 minutes after a serious injury, during which prompt care can significantly enhance survival chances.
Health Monitoring Integrated into Workflow
Beyond accident detection, the wearable device continuously monitors essential health indicators, including:
- Heart rate
- Body temperature
- Oxygen saturation
- Blood pressure
Workers can log their baseline health data before starting their shifts. Any deviations during work trigger alerts, allowing supervisors to intervene early, marking a shift from reactive safety to preventive care embedded within daily operations.
Machine Learning for Fall Classification
The system employs accelerometers and gyroscopes, combined with machine learning models, to analyze movement patterns. It can distinguish between routine slips and critical falls, including estimating fall height. The team reports that the models have been trained on diverse real-world activities and achieve over 98% accuracy. Processing occurs directly on the device, eliminating reliance on cloud systems and ensuring instant alerts.
Designed for Large-Scale Industrial Deployment
To facilitate deployment across large facilities, the system utilizes LoRa (Long Range) wireless communication, which allows for stable data transmission with low power consumption. The wearable device has undergone field testing at a thermal power plant in Ramagundam and multiple construction sites in Hyderabad, where workers have utilized it during routine operations.
Support and Future Development
The project has garnered support from the Department of Science and Technology and has reached an advanced stage of development, with multiple research publications and patents. The team is currently focused on refining the system for large-scale industrial adoption.
SOS Feature for Manual Emergency Alerts
In addition to automated detection capabilities, the device includes an SOS button that enables workers to send immediate alerts if they are conscious and in distress. The monitoring system continuously displays worker data, ensuring that supervisors can respond quickly to emergencies. The developers emphasize that the system aims to reduce reliance on manual reporting and ensure that accidents are detected and addressed without delay.
Conclusion
The development of this wearable safety system by IIIT Hyderabad represents a significant advancement in industrial safety technology. By integrating real-time monitoring, health tracking, and machine learning, the GoldAid system aims to enhance workplace safety and improve emergency response times in hazardous environments.
Note: This article is based on information available as of April 2026 and may be subject to updates and changes as the technology evolves.
