MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641096425 A) filed by Rajalakshmi Engineering College on August 10, 2026, for Neuro-Adaptive Learning Device.
Inventor includes Dr. K. Ananthajothi.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: The present invention relates to a Neuro-Adaptive Learning Device configured to dynamically control an electronic learning interface according to objectively measured neurophysiological and physiological responses of a learner. The device comprises a physiological sensing subsystem having one or more sensors for acquiring signals including electroencephalographic activity, electrodermal activity, photoplethysmographic or heart-rate-related signals and eye-response parameters. A signal acquisition and conditioning unit digitizes and filters the acquired signals and a preprocessing and feature extraction processor suppresses artefacts and derives physiological features. An artificial-intelligence-based cognitive-state estimation engine processes the extracted features to determine one or more learner states including attention, cognitive workload, engagement, fatigue or stress. The determined cognitive-state parameters are combined with measurable learning-performance information to generate a multimodal learner-state representation. An adaptive control engine converts the learner-state representation into device-control parameters for automatically modifying content complexity, presentation rate, modality, assessment difficulty, repetition, hints or related interface parameters. Physiological signals are reacquired after each adaptation to establish a closed feedback loop between the learner, sensing hardware, processing subsystem and adaptive learning interface. Learner-specific calibration enables adaptation to individual physiological variations, while local processing may reduce latency and unnecessary transmission of raw physiological information. The invention thereby provides an integrated physical and computational architecture for real-time neuro-adaptive human-machine interaction in electronic learning environments.
Disclaimer: Curated by HT Syndication.
