Terrain Modeling for Control of Lower Limb Prostheses and Exoskeletons Using Low-Cost Wearable Sensors

The development of powered lower-limb pros-theses and exoskeletons (LLPE) for assisting individuals in activities of daily living has been gaining increasing interest in the robotic community. To assist wearers walking on various terrains in daily environments, accurate gait-mode recognition and seamless transition of control strategies are crucial for these devices. Due to the high diversity of terrains, such capabilities are usually subject to terrain conditions, making terrain detection an essential issue for LLPE control. In the paper, we proposed a method for terrain detection and modeling aimed at reconstructing terrains rather than merely classifying them to provide richer information for LLPE control, including online 2D terrain information and the relative foot position. The implementation of the proposed method relies on a sensor group consisting of a low-cost single-point laser sensor and two inertial measurement units (IMU) to simultaneously and continuously capture lower limb kinematic features and terrain features. A time-varying Kalman filter is employed to fuse these features, facilitating rapid and accurate modeling of different terrains such as level ground, stairs ascend/descend, and ramp ascend/descend. The performance of the proposed method was evaluated via experiments with two healthy subjects. The results show that the reconstructed terrains can provide accurate information for LLPE control, demonstrating the effectiveness and adaptability of the proposed method.