title: Noncontact Capacitive Sensing-Based Locomotion Transition Recognition for Amputees With Robotic Transtibial Prostheses.
abstract: Recent advancement of robotic transtibial prostheses can restore human ankle dynamics in different terrains. Automatic locomotion transitions of the prosthesis guarantee the amputee's safety and smooth motion. In this paper, we present a noncontact capacitive sensing-based approach for recognizing locomotion transitions of amputees with robotic transtibial prostheses. The proposed sensing system is designed with flexible printed circuit boards which solves the walking instability brought by our previous system when using robotic prosthesis and improves the recognition performance. Six transtibial amputees were recruited and performed tasks of ten locomotion transitions with the robotic prosthesis that we recently constructed. The capacitive sensing system was integrated on the prosthesis and worked in combination with on-prosthesis mechanical sensors. With the cascaded classification method, the proposed system achieved 95.8% average recognition accuracy by support vector machine (SVM) classifier and 94.9% accuracy by quadratic discriminant analysis (QDA) classifier. It could accurately recognize the upcoming locomotion modes from the stance phase of the transition steps. In addition, we proved that adding capacitance signals could significantly reduce recognition errors of the robotic prosthesis in locomotion transition tasks. Our study suggests that the fusion of capacitive sensing system and mechanical sensors is a promising alternative for controlling the robotic transtibial prosthesis.
authors.forename: Enhao
authors.initials: E
authors.lastname: Zheng
authors.forename: Qining
authors.initials: Q
authors.lastname: Wang
mesh_terms: D056044:Actigraphy / Q000295:instrumentation*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000674:Amputees / Q000534:rehabilitation*; D001186:Artificial Limbs; D003217:Conductometry / Q000295:instrumentation; D025802:Electric Capacitance; D004867:Equipment Design; D019544:Equipment Failure Analysis; D000067190:Exoskeleton Device; D020233:Gait Disorders, Neurologic / Q000503:physiopathology* / Q000534:rehabilitation*; D006801:Humans; D008124:Locomotion; D008297:Male; D008875:Middle Aged; D000066530:Neurological Rehabilitation / Q000295:instrumentation; D015203:Reproducibility of Results; D012371:Robotics / Q000295:instrumentation; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D013813:Therapy, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D016896:Treatment Outcome