A Wearable IoT-Based System for Gait Cycle Duration and Symmetry Assessment in Lower-Limb Amputees

This study presents the development of a wearable sensor designed to assess gait asymmetry. The device uses the TF mini-S LiDAR sensor to detect mid-stance and calculates the gai cycle duration to assess symmetry. The Arduino Nano RP2040 Connect micro controller was chosen for its compact size, real-time data processing capabilities, and seamless integration with the Arduino Cloud, facilitating remote monitoring and data storage. The TF mini-S LiDAR was found to be the most suitable sensor due to its field of view and high frame rate, allowing accurate mid-stance detection. The final device combines portability, high data update rates, and precise detection, making it an effective tool for monitoring gait rehabilitation, including LLA. This wearable sensor contributes to improving prosthetic adaptation and offers healthcare systems a reliable means of monitoring patient progress.