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Cited by 425

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

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

Open access
Varol, H.A., Sup, F., Goldfarb, M.·2010·IEEE Transactions on Biomedical Engineering

Abstract

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.

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Research works printed but not linked: 6

  1. mitchell, 1997, Machine Learning
  2. 10.1109/BIOROB.2008.4762919 · doi:10.1109/biorob.2008.4762919
  3. popovic, 1988, Electrophysiological Kinesiology, 337
  4. nagano, 1998, comparison of new approaches to estimate mechanical output of individual joints in vertical jumps, J Biomech, 31, 951, 10.1016/S0021-9290(98)00094-3 · doi:10.1016/s0021-9290(98)00094-3
  5. 10.1016/0021-9290(95)00083-6 · doi:10.1016/0021-9290(95)00083-6
  6. 10.1016/0021-9290(95)00115-8 · doi:10.1016/0021-9290(95)00115-8

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Metadata

Authors
Varol, H. A., Sup, F., & Goldfarb, M.
Venue
IEEE Transactions on Biomedical Engineering
Published
2010-03
Volume
57
Issue
3
Pages
542-551
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
OpenAlex
W2144910137
Cited by
425
References
26
FWCI
5.43
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis. https://app.cochat.ai/papers/10.1109/tbme.2009.2034734
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Varol, H. A., Sup, F., & Goldfarb, M. (2010). Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2009.2034734

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