Real-time gait mode intent recognition of a powered knee and ankle prosthesis for standing and walking

Huseyin Atakan Varol, Frank Sup, Michael Goldfarb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Citations (Scopus)

Abstract

This paper describes a real-time gait mode intent recognition approach for the supervisory control of a powered transfemoral prosthesis. The proposed approach infers user intent by recognizing patterns in the prosthesis sensor's signals in real-time, eliminating the need for sound-side instrumentation and allowing fast mode switching. Simple time based features extracted from frames of prosthesis signals are reduced to lower dimensions. Gaussian Mixture Models are trained using an experimental database for gait mode classification. A voting scheme is applied as a post-processing step to increase the robustness of decision making. The effectiveness of the proposed method is shown via gait experiments on a treadmill with a healthy subject using an able bodied adapter.

Original languageEnglish
Title of host publicationProceedings of the 2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2008
Pages66-72
Number of pages7
DOIs
Publication statusPublished - 2008
Event2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2008 - Scottsdale, AZ, United States
Duration: Oct 19 2008Oct 22 2008

Publication series

NameProceedings of the 2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2008

Other

Other2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2008
CountryUnited States
CityScottsdale, AZ
Period10/19/0810/22/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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