• Rishab R Kumar
• Sujay S Bhandari
• Sushit R Mapari
• Vivek G Naik
• Dr. Sandeep Bhat

Rapid Response, a clever emergency help system designed for people with impairments, especially those with restricted mobility, is presented in this study. In order to facilitate natural human-computer interaction, the system incorporates cutting-edge computer vision algorithms using OpenCV and MediaPipe for real-time facial and hand gesture detection. To guarantee prompt notifications to emergency contacts and caregivers, it integrates these functionalities with strong emergency communication capabilities driven by Twilio and Telegram APIs. To improve accessibility, the system architecture incorporates a bilingual interface, a responsive HTML5 and JavaScript frontend, and a Flask-based backend. Customizable gesture recognition, automated emergency alerts, and a caregiver dashboard for real-time monitoring are some of the main features. Significant gains in accuracy and response time over conventional systems are shown via experimental evaluation. Future scalability is supported by the modular architecture, which integrates cutting-edge technologies like AI, IoT.

Assistive technology, gesture recognition, computer vision, emergency system, disabled users, Twilio, MediaPipe, OpenCV.

"Rapid Response: A Smart Emergency Assistance System for Disabled Individuals Using Gesture Recognition and Real-Time Communication", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.381-385, July-2025, Available :http://www.jetir.org/papers/JETIRGX06069.pdf

"Rapid Response: A Smart Emergency Assistance System for Disabled Individuals Using Gesture Recognition and Real-Time Communication", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp381-385, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06069.pdf