MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641067421 A) filed by Dr. K. R. Jansi; Akhila Tejaswi Y; and B Karthik on May 29, 2026, for Cognivue: A Real Time Multimodal System For Behavioral And Cognitive Fatigue Detection In Workplace Environments.
Inventors include Dr. K. R. Jansi; Akhila Tejaswi Y; and B Karthik.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: Fatigue, emotional strain, and loss of attention in the workplace have a great effect on the wellbeing, productivity, and safety of employees, especially in workplaces with high cognitive load and interaction with customers. Conventional methods of monitoring are either intrusive, one dimensional or reactive which restricts its effectiveness in a real-life organizational 1 0 context. The proposed project is a multi modal system of behavioral and cognitive state monitoring in real-time with the use of AI and including fatigue detection, emotion measurement, attention measurement, and intelligent job reallocation through the application of computer vision and deep learning methods. The suggested system takes advantage of nonobtrusive webcamifiers, visual reports, to scan the facial expression, eye movement, head 15 position, and gaze patterns. Fatigue is measured using accepted visual features like Rye Aspect Ratio (EAR), blink rate, PERCLOS, mouth aspect ratio (yawning), and head nodding, and mood is determined using a CNN-based facial emotion recognition model, which may be further expanded to include valence-arousal estimation to continuously analyse mood. It is concluded that attention and distraction are based on the presence of a face, based on the 20 duration of head orientation, and gaze direction. Multi-window analysis and temporal smoothing are used to achieve stability and trend-based evaluation. The real- time metrics are sent out in real time over Server-Sent Events (SSE) and plotted to display an interactive dashboard, which allows tracking the level of fatigue, emotional patterns, and the scores of attention. These insights are converted into actionable outputs such as alerts, wellbeing scores, break recommendations, and work reallocation or job reallocation recommendations through a rule based recommendation and decision layer to balance the workload of employees with their existing cognitive and emotional capacity. The system will be supportive and ethical with the focus on transparency, preservation of privacy, and non-punitive intervention. The extensions in the future are machine learning-based risk prediction and large-scale organizational analytics.
Disclaimer: Curated by HT Syndication.
