Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots

• Published in: IEEE/ASME transactions on mechatronics Vol. 22; no. 6; pp. 2480 - 2491
• Main Authors: Parri, Andrea, Yuan, Kebin, Marconi, Dario, Tingfang Yan, Crea, Simona, Munih, Marko, Lova, Raffaele Molino, Vitiello, Nicola, Qining Wang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biomechanics; Fuzzy logic; Fuzzy-logic classifier; gait assistance; Hip; Kinematics; Legged locomotion; Locomotion; locomotion mode recognition (LMR); lower limb wearable robots; Mechatronics; Medical robotics; Orthoses; Pressure sensors; Real time; Robot sensing systems; Robots; Training; Wearable computing; Wearable technology
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2017.2755048

Real-time recognition of locomotion-related activities is a fundamental skill that a controller of lower limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for real-time locomotion mode recognition of locomotion-related activities in lower limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a time-based approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy-logic method triggered by foot pressure sensors operates in a subject-independent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10 000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities.