IoT-Enabled Glove for Women’s Safety and Sign Language Translation

This paper presents the design and implementation of an IoT-enabled multifunctional glove intended for enhancing women’s safety and enabling sign language translation. The system incorporates flex sensors to capture finger bending, a GPS module for real-time location tracking, and a distress alert system activated by a predefined gesture. The Raspberry Pi serves as the central processor, managing data from the Arduino Nano, executing gesture classification using the k-Nearest Neighbors algorithm (k =3), and communicating with cloud services and mobile devices. The glove achieves a gesture recognition accuracy of 96.1%, with an emergency alert success rate of 100% across 50 trials and an average response time of 3.14 seconds. For assistive communication, detected gestures are converted to text or synthesized speech using onboard text-to-speech modules. The device remains functional after five 60C wash cycles, confirming its durability and washability. The glove supports continuous operation for up to 8.2 hours using a 3.7 V, 60 Ah rechargeable battery. The system offers a dual-purpose, lightweight, and ergonomic solution for real-time safety intervention and accessible communication.