Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis

• Published in: IEEE transactions on biomedical engineering Vol. 57; no. 3; pp. 542 - 551
• Main Authors: Varol, Huseyin Atakan, Sup, Frank, Goldfarb, Michael
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic sensors; Artificial Intelligence; Artificial Limbs; Biological and medical sciences; Computational efficiency; Control systems; Controllers; Data mining; Electric Impedance; Electronics, Medical - instrumentation; Electronics, Medical - methods; Feature extraction; Humans; Instrumentation; Instruments; Leg; Leg - physiology; Legged locomotion; Limbs; Male; Mathematical models; Medical sciences; Miscellaneous; Orthopedic surgery; Pattern recognition; Pattern Recognition, Automated - methods; physical human-robot interaction; powered prosthesis; Prosthetics; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Real time; Recognition; rehabilitation robotics; Robotics - instrumentation; Robotics - methods; Signal Processing, Computer-Assisted; Supervisory control; Surgery (general aspects). Transplantations, organ and tissue grafts. Graft diseases; Surgical implants; Technology. Biomaterials. Equipments. Material. Instrumentation; Walking - physiology; Young Adult; Performance evaluation; Human; powered prosthesis; rehabilitation robotics; Prosthesis; Instrumentation therapy; Lower limb; Instrumentation; Male; physical human―robot interaction; Pattern recognition; Experimental study; Robotics; Reeducation; User interface; Young adult; Amputation; Rehabilitation(human); Robot; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2009.2034734

This paper describes a control architecture and intent recognition approach for the real-time supervisory control of a powered lower limb prosthesis. The approach infers user intent to stand, sit, or walk, by recognizing patterns in prosthesis sensor data in real time, without the need for instrumentation of the sound-side leg. Specifically, the intent recognizer utilizes time-based features extracted from frames of prosthesis signals, which are subsequently reduced to a lower dimensionality (for computational efficiency). These data are initially used to train intent models, which classify the patterns as standing, sitting, or walking. The trained models are subsequently used to infer the user's intent in real time. In addition to describing the generalized control approach, this paper describes the implementation of this approach on a single unilateral transfemoral amputee subject and demonstrates via experiments the effectiveness of the approach. In the real-time supervisory control experiments, the intent recognizer identified all 90 activity-mode transitions, switching the underlying middle-level controllers without any perceivable delay by the user. The intent recognizer also identified six activity-mode transitions, which were not intended by the user. Due to the intentional overlapping functionality of the middle-level controllers, the incorrect classifications neither caused problems in functionality, nor were perceived by the user.