Fast Gait Mode Detection and Assistive Torque Control of an Exoskeletal Robotic Orthosis for Walking Assistance

• Published in: IEEE transactions on robotics Vol. 34; no. 4; pp. 1035 - 1052
• Main Authors: Weiguang Huo, Mohammed, Samer, Amirat, Yacine, Kyoungchul Kong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuation; Artificial Intelligence; Ascent; Assistive torque control; Automatic; Computer Science; Computer simulation; Descent; Electromyography; Engineering Sciences; Exoskeletons; Foot; Fuzzy logic; Fuzzy systems; Gait; gait mode detection; human motion assistance; Kinematics; Knee; Legged locomotion; lower limb exoskeleton; Orthoses; Robot sensing systems; Robotics; Robots; Systems and Control; Torque; Walking; Human motion assistance; Lower limb exoskeleton; Assistive torque control; Gait mode detection
• ISSN: 1552-3098; 1941-0468
• DOI: 10.1109/TRO.2018.2830367

Gait modes, such as level walking, stair ascent/descent, and ramp ascent/descent, show different lower-limb kinematic and kinetic characteristics. Therefore, an accurate detection of these modes is critical for a wearable robot to provide appropriate power assistance. In this paper, a fast gait-mode-detection method based on a body sensor system is proposed. A fuzzy logic algorithm is used to estimate the likelihoods of gait modes in real time. Since the proposed fast gait mode detection makes it possible to select appropriate kinematic and kinetic models for each gait mode, assistive torques required for assisting the human motions can be obtained more naturally and immediately. The proposed methods are all verified by experiments with a lower-limb exoskeletal assistive robot with transparent actuation by series elastic actuators, called the exoskeletal robotic orthosis for walking assistance. Four healthy subjects participated in the experiments. All subjects were asked to perform different gait modes using their normal and simulated abnormal gaits, i.e., blocking the knee joint of one leg during walking. Latency and success rate of gait mode detection are selected as performance criteria. The effectiveness of the proposed gait-mode-based assistive strategy is evaluated using electromyography muscular activities.