A CNN-Based Method for Intent Recognition Using Inertial Measurement Units and Intelligent Lower Limb Prosthesis

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 5; pp. 1032 - 1042
• Main Authors: Su, Ben-Yue, Wang, Jie, Liu, Shuang-Qing, Sheng, Min, Jiang, Jing, Xiang, Kui
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Amputation; Amputees; Ankle; Artificial Limbs; Artificial neural networks; Ascent; Biomechanical Phenomena; Calibration; convolutional neural networks (CNN); Data analysis; Descent; Electromyography; Empirical analysis; Humans; inertial measurement unit (IMU); Inertial platforms; Intent recognition; Intention; Knee; Learning algorithms; Leg; Legged locomotion; Locomotion; lower limb prosthesis; Machine Learning; Male; Mapping; Measurement units; Mechanical sensors; Neural networks; Neural Networks, Computer; Pattern recognition; Prostheses; Prosthesis Design; Prosthetics; Recognition, Psychology; Steady state; swing phase; Trans-femoral amputees; Walking

Powered intelligent lower limb prosthesis can actuate the knee and ankle joints, allowing transfemoral amputees to perform seamless transitions between locomotion states with the help of an intent recognition system. However, prior intent recognition studies often installed multiple sensors on the p...