Footstep Induced Signal

Footstep-induced signals, encompassing pressure, vibration, and kinematic data from footfalls, are being extensively studied to understand and improve locomotion in robots and humans. Current research focuses on developing robust algorithms, such as model predictive control (MPC) and neural networks (including CNNs and RNNs), for real-time footstep planning and adaptation to challenging terrains and disturbances, often incorporating full-body dynamics and terrain awareness. These advancements are crucial for enhancing the stability and adaptability of legged robots, and for applications in human gait analysis, health monitoring, and human-computer interaction, particularly in detecting conditions like freezing of gait. The ultimate goal is to create more agile and robust robots and more accurate diagnostic tools for human movement disorders.