title: Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion.
abstract: 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.
authors.affiliation: Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA. huang@ele.uri.edu
authors.forename: He
authors.initials: H
authors.lastname: Huang
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.forename: Levi J
authors.initials: LJ
authors.lastname: Hargrove
authors.forename: Zhi
authors.initials: Z
authors.lastname: Dou
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Rogers
authors.forename: Kevin B
authors.initials: KB
authors.lastname: Englehart
mesh_terms: D000674:Amputees / Q000534:rehabilitation; D001186:Artificial Limbs; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008124:Locomotion / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D013848:Thigh