FlexLingo is a final year IoT project that serves as a flex sensor-based sign language translator. It uses Random Forest and BiLSTM models to interpret real-time hand gestures, translating them into spoken words or text via an Arduino smart glove and a React web interface.

Sign language recognition using flex sensors, gyroscope, accelerometer and raspberry pi mounted on glove

An assistive technology system enabling mute individuals to communicate through hand gestures. Uses flex sensors with Arduino to capture finger movements, KNN machine learning for real-time gesture classification, and text-to-speech for voice output. Supports 11 predefined phrases with expandable gesture library.

A real-time embedded system for intelligent sitting posture detection and monitoring using ESP32, machine learning, and multimodal sensor fusion to promote ergonomic health and prevent musculoskeletal disorders.

Converts sign language gestures into speech using flex sensors and DFPlayer Mini on Arduino.