Continuous locomotion-mode identification for prosthetic legs based on neuromuscular - Mechanical fusion

He Huang*, Fan Zhang, Levi J. Hargrove, Zhi Dou, Daniel R. Rogers, Kevin B. Englehart

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

286 Scopus citations


In this study, we developed an algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations. Electromyographic (EMG) signals recorded from gluteal and residual thigh muscles and ground reaction forces/moments measured from the prosthetic pylon were used as inputs to a phase-dependent pattern classifier for continuous locomotion-mode identification. The algorithm was evaluated using data collected from five patients with TF amputations. The results showed that neuromuscular-mechanical fusion outperformed methods that used only EMG signals or mechanical information. For continuous performance of one walking mode (i.e., static state), the interface based on neuromuscular-mechanical fusion and a support vector machine (SVM) algorithm produced 99 or higher accuracy in the stance phase and 95 accuracy in the swing phase for locomotion-mode recognition. During mode transitions, the fusion-based SVM method correctly recognized all transitions with a sufficient predication time. These promising results demonstrate the potential of the continuous locomotion-mode classifier based on neuromuscular-mechanical fusion for neural control of prosthetic legs.

Original languageEnglish (US)
Article number5951743
Pages (from-to)2867-2875
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number10 PART 1
StatePublished - Oct 2011


  • Data fusion
  • electromyography (EMG)
  • pattern recognition
  • prosthesis
  • surface electromyography

ASJC Scopus subject areas

  • Biomedical Engineering


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