MUMBAI, India, June 30 -- Intellectual Property India has published a patent application (202641073630 A) filed by Prince Shri Venkateshwara Padmavathy Engineering College; Prince Dr. K. Vasudevan College Of Engineering And Technology; Prince Shri Balaji Arts And Science College; and Prince Shri Venkateshwara Arts And Science College on June 13, 2026, for An Artificial Intelligence Enabled Iot Wearable System And Method For Predictive Fall Prevention, Real-Time Fall Detection, Posture Analysis, Location Tracking And Emergency Caregiver Alert Generation.
Inventors include Dr. A. R Aravind; Dr. P Sharmila; Akshaya S; Harish Kumar S P; Priyadharshini K; Janarthanan V; and Manoj S.
The application for the patent was published on June 26, 2026, under issue no. 26/2026.
Abstract: The present invention relates to an Artificial Intelligence (AI) enabled Internet of Things (IoT) wearable system and method for predictive fall prevention, real-time fall detection, posture analysis, location tracking, physiological monitoring, and emergency caregiver alert generation. The proposed system comprises a wearable sensor module including an inertial measurement unit having a three-axis accelerometer and gyroscope, a processing unit, a predictive fall risk assessment module, a physiological monitoring module, a Global Positioning System (GPS) tracking module, a hybrid communication module, a cloud processing server, and a caregiver monitoring application. The system continuously acquires motion, posture, physiological, and location data from a user and processes the acquired data to identify abnormal movement patterns, posture deviations, and potential fall risks. A Fall Risk Index (FRI) is generated using gait stability, acceleration characteristics, posture behaviour, and historical activity information to predict potential fall events before their occurrence. Actual fall events are detected using a dual-condition verification process involving impact detection and free-fall analysis, thereby reducing false alarms. Upon detection of an emergency condition, the system automatically transmits notifications, user status information, and geographical coordinates to caregiver devices through Wi-Fi, cellular, or Bluetooth communication channels. The hybrid communication architecture ensures reliable alert delivery by automatically switching between available communication networks. The system further incorporates geo- fencing functionality for monitoring predefined safe zones and generating alerts when a user moves beyond designated boundaries. Physiological parameters including heart rate, blood oxygen saturation, and body temperature may be analyzed in conjunction with motion and posture information for enhanced health monitoring. The invention further includes a caregiver escalation mechanism configured to sequentially notify multiple caregivers when emergency alerts remain unacknowledged. The proposed system provides an intelligent, scalable, and cost-effective solution for elderly care, remote patient monitoring, and personal safety applications by enabling predictive risk assessment, rapid emergency response, and continuous health surveillance.
Disclaimer: Curated by HT Syndication.
