Environmental Features Recognition for Lower Limb Prostheses Toward Predictive Walking

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 3; pp. 465 - 476
• Main Authors: Zhang, Kuangen, Xiong, Caihua, Zhang, Wen, Liu, Haiyuan, Lai, Daoyuan, Rong, Yiming, Fu, Chenglong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Amputation; Amputees; Artificial Limbs; Cameras; Classification; Computer Simulation; Databases, Factual; Environment; environmental features recognition; Estimation; Experiments; Feature recognition; Female; Healthy Volunteers; Humans; Indoor environments; Inertial platforms; Inertial sensing devices; Legged locomotion; Locomotion; locomotion modes prediction; Male; Neural networks; Neural Networks, Computer; Predictive control; predictivewalking; Prostheses; Prosthesis Design; Prosthetics; Three dimensional models; Three-dimensional displays; Trans-femoral amputees; Two dimensional displays; vision; Vision sensors; Walking; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2019.2895221

This paper aims to present a robust environmental features recognition system (EFRS) for lower limb prosthesis, which can assist the control of prosthesis by predicting the locomotion modes of amputees and estimating environmental features in the following steps. A depth sensor and an inertial measurement unit are combined to stabilize the point cloud of environments. Subsequently, the 2D point cloud is extracted from origin 3D point cloud and is classified through a neural network. Environmental features, including slope of road, width, and height of stair, were also estimated via the 2D point cloud. Finally, the EFRS is evaluated through classifying and recognizing five kinds of common environments in simulation, indoor experiments, and outdoor experiments by six healthy subjects and three transfemoral amputees, and databases of five healthy subjects and three amputees are used to validate without training. The classification accuracy of five kinds of common environments reach up to 99.3% and 98.5% for the amputees in the indoor and outdoor experiments, respectively. The locomotion modes are predicted at least 0.6 s before the switch of actual locomotion modes. Most estimation errors of indoor and outdoor environments features are lower than 5% and 10%, respectively. The overall process of EFRS takes less than 0.023 s. The promising results demonstrate the robustness and the potential application of the presented EFRS to help the control of lower limb prostheses.