MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202621083169 A) filed by Jainish Chandresh Gotecha; Dr. Rachna Desai; Nirali Mehta; Dr. Sujata T. Bhainallykar; Prof. Nilofar Tamboli; Jayashree Pravin Chaudhari; Dhanashree Suryabhan Ingle; and Sonali Sambare on July 06, 2026, for Ai Based Smart Helmet With Real Time Accident Detection And Alert System Using Iot.
Inventors include Jainish Chandresh Gotecha; Dr. Rachna Desai; Nirali Mehta; Dr. Sujata T. Bhainallykar; Prof. Nilofar Tamboli; Jayashree Pravin Chaudhari; Dhanashree Suryabhan Ingle; and Sonali Sambare.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: The disproportionate fatality rate among two-wheeler riders is fundamentally exacerbated by the inherent passivity of conventional protective headgear and the critical latency in post-crash emergency medical response. While rudimentary sensor integrations have historically attempted to automate distress signaling, their reliance on deterministic, threshold-based logic frequently results in debilitating false-positive alerts triggered by routine vehicular kinematics. To resolve these systemic vulnerabilities, this research proposes an edge-optimized smart helmet architecture that seamlessly integrates biometric compliance verification, continuous kinematic surveillance, and autonomous Internet of Things (IoT) telemetry. Proactively, the system introduces a pre-ignition safety interlock utilizing infrared proximity and semiconductor gas sensors to mathematically enforce helmet utilization and rider sobriety before authorizing vehicular operation. Reactively, an embedded Inertial Measurement Unit (IMU) continuously harvests multidimensional spatial data during transit. This high-dimensional time-series data is processed entirely on-device by a lightweight machine learning algorithm, which is trained to definitively distinguish the chaotic spatio-temporal signatures of a catastrophic collision from standard operational anomalies, thereby neutralizing false-positive triggers. Upon algorithmic verification of a crash event, the architecture bypasses the need for conscious human intervention, autonomously extracting precise GPS coordinates and broadcasting an encrypted distress payload via cellular networks to pre-configured contacts and emergency dispatchers.
Disclaimer: Curated by HT Syndication.
