MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641070815 A) filed by Chigilipalli Dhilleswara Rao; Gudivada Bindu Bhargav; and Yekeswar Dev on June 07, 2026, for A Self-Calibrating Edge Wearable Helmet For Predictive Health Monitoring And Localization”.
Inventors include Chigilipalli Dhilleswara Rao; Gudivada Bindu Bhargav; Yekeswar Dev; and Malli Mahesh Koushil.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: Self-Calibrating Edge Wearable Helmet for Predictive Health Monitoring and Localization The present invention discloses a self-calibrating, edge-enabled wearable helmet system designed for continuous, real-time monitoring and predictive analysis of physiological conditions. The system integrates a multi-modal biosensing network, including electroencephalogram (EEG) and electrocardiogram (ECG) sensors, within a compact helmet architecture to acquire neurological and cardiac signals simultaneously. An embedded edge computing unit equipped with artificial intelligence (AI) capabilities processes the acquired data locally, enabling low-latency, privacy-preserving, and offline health diagnostics without reliance on cloud infrastructure. A key innovation of the invention lies in its adaptive self-calibration mechanism, which dynamically adjusts sensor parameters such as gain, filtering thresholds, and sensitivity based on real-time signal quality, environmental conditions, and user-specific variations. This ensures consistent accuracy and reliability across diverse usage scenarios without requiring manual intervention or expert calibration. The system further incorporates advanced machine learning and deep learning models, including time-series analysis and anomaly detection algorithms, to perform predictive health analytics. By leveraging multi-modal data fusion techniques, the invention enhances the detection of complex physiological patterns and enables early identification of pre-symptomatic neurological disorders, cardiac abnormalities, and seizure risks. The AI engine is further capable of adaptive learning, continuously refining its predictive models based on user-specific physiological baselines through incremental or federated learning approaches. In addition, the helmet integrates a localization module, such as a Global Positioning System (GPS), coupled with a wireless communication interface to provide real-time location tracking and automated emergency alert transmission. Upon detection of abnormal physiological conditions, the system generates alerts and transmits both health status and location data to predefined contacts, healthcare providers, or remote monitoring systems. The invention also incorporates energy-efficient edge processing techniques, wherein computational resources, sensor sampling rates, and power consumption are dynamically optimized based on detected signal activity, thereby extending battery life and enabling prolonged operation. The fully integrated wearable design enables continuous, non-invasive monitoring in both clinical and non-clinical environments, making it suitable for remote healthcare, emergency response, occupational safety, and personal health management applications. Overall, the proposed system represents a significant advancement over conventional diagnostic methods by combining self-calibration, edge AI, multi-sensor fusion, predictive analytics, and real-time localization into a single portable platform, thereby enabling early detection, timely intervention, and improved healthcare outcomes.
Disclaimer: Curated by HT Syndication.
