Estimation of Lower Extremity Muscle Activation during Walking using Wearable Inertial Sensor Network and Neural Networks

This thesis explores how wearable sensors and artificial intelligence can estimate muscle activity in the legs during walking. By attaching compact devices to the body segments, the system tracks movement data to predict muscle activation during walking, eliminating the need for invasive techniques like EMG. This innovation simplifies gait analysis, making it easier to monitor walking patterns in real time. This technology enables real-time gait monitoring in clinical and home settings, offering insights for rehabilitation and elder care.