Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion
By He (Helen) Huang, Fan Zhang, Levi J. Hargrove, Zhi Dou, Daniel R. Rogers, Kevin B. Englehart. IEEE Transactions on Biomedical Engineering, 2011
508 citations recorded by the source corpus.
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.
Subfields: Biomedical Engineering
View Paper: https://www.semanticscholar.org/paper/8b0b9a20e559bdf146a7651f90ec563e63e87df0
Citations in the corpus
- Control strategies for active lower extremity prosthetics and orthotics: a review (2015, unknown): “Also interesting is the electromechanical delay between the motor commands and the generation of force in the muscle on the order of 10s of milliseconds [58], which would provide a significant head-start to a controller based on muscle activity over one based on mechanical feedback alone [59].”
- Autonomy in Rehabilitation Robotics: An Intersection (2018, unknown): “force sensors, joint encoders) (39) or EMG data (37); and today, most often a combination of both (38, 49).”