MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641096394 A) filed by Rajalakshmi Engineering College on August 10, 2026, for An Adaptive Multi-Sensor Biometric Locking System With Iot-Enabled Behavioral Anomaly Detection.
Inventors include Dr. P. Kumar; and C. Harisainath.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: The present invention discloses a Biometric ToT Lock with Adaptive Learning that delivers an advanced, secure, and intelligent access control solution through hardware-centric behavioral anomaly detection. The invention overcomes the inherent limitations of conventional biometric locks, which rely on static, one-time identity verification and remain vulnerable to spoofing, coercive access, and abnormal usage patterns. The proposed system integrates biometric sensing hardware, including fingerprint and/or facial recognition sensors, with behavioral sensing hardware such as pressure-sensitive floor sensors positioned near an access point. These sensors generate multi-modal physical signals representing both physiological identity and user behavior. An embedded processing unit receives the sensor signals and performs signal conditioning, feature extraction, and synchronization, enabling real-time analysis suitable for physical lock control. An adaptive learning and decision engine, executed on embedded or edge hardware, is trained during an enrollment phase to learn normal access behavior of authorized users. The learned behavioral model includes parameters such as access time patterns, gait pressure distribution, step cadence, and frequency of entry. During operational use, the system continuously evaluates live biometric and behavioral data against stored profiles to detect deviations indicative of anomalous or potentially unauthorized access. The decision engine generates control signals that directly influence an electromechanical locking mechanism, permitting or inhibiting physical lock actuation. Upon detection of anomalous behavior, the system initiates security responses including access denial, alert notifications, or invocation of secondary authentication mechanisms via an IoT communication interface. The adaptive learning model is incrementally updated using validated access events to accommodate gradual behavioral changes while maintaining robust security. The invention provides a technical solution to the technical problem of improving physical access security, producing a clear technical effect in terms of enhanced lock reliability, reduced false acceptance, and real-time anomaly-aware authentication. The inventive contribution lies in the functional integration of sensing hardware, embedded processing, adaptive learning, and physical lock actuation, and is suitable for deployment in residential, commercial, industrial, and critical infrastructure environments.
Disclaimer: Curated by HT Syndication.
