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

By Huseyin Atakan Varol, Frank C. Sup, Michael Goldfarb. IEEE Transactions on Biomedical Engineering, 2009

442 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/bbd3f65713dd85f0ceae2c6066754a3788a2bfdd

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Citations in the corpus

Autonomy in Rehabilitation Robotics: An Intersection (2018, unknown): “On the shared control of an EMG- controlled prosthetic hand: Analysis of user-prosthesis interaction.”
Control strategies for active lower extremity prosthetics and orthotics: a review (2015, unknown): “[24] employed a GMM to switch between sitting, walking, and standing modes using the embedded sensors in an actuated transfemoral prosthesis.”
A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human-Robot Interaction (2018, methodology): “[42] considers this problem for lower-limb prosthetics.”
Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion (2011, methodology): “chanical measurements from prosthetic legs [8], fusing both data sources may further improve the accuracy of locomotion-mode identification.”
A Survey of Robots in Healthcare (2021, background): “There has also been significant research in the development of an intelligent prosthesis for use by amputees [125].”