A Classification Approach Based on Directed Acyclic Graph to Predict Locomotion Activities With One Inertial Sensor on the Thigh

• Published in: IEEE transactions on medical robotics and bionics Vol. 3; no. 2; pp. 436 - 445
• Main Authors: Papapicco, V., Chen, B., Munih, M., Davalli, A., Sacchetti, R., Gruppioni, E., Crea, S., Vitiello, N.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active control; Algorithms; Body parts; Classification; Classifiers; directed acyclic graph; Feature extraction; Graph theory; inertial measurement unit; Inertial platforms; Inertial sensing devices; Legged locomotion; Locomotion; Locomotion mode classification; machine learning; Prostheses; Robot sensing systems; Sensors; Stairs; Steady state; support vector machines; Thigh

Current state-of-the-art locomotion mode classifiers for controlling robotic lower-limb prostheses rely on multiple sensors to achieve high accuracy, prediction performance, and robustness to both speed changes and subject-specific gait patterns. However, multiple sensors placed on different body pa...