L
Levi J. Hargrove
Researcher at Northwestern University
Publications - 219
Citations - 9912
Levi J. Hargrove is an academic researcher from Northwestern University. The author has contributed to research in topics: Ankle & Medicine. The author has an hindex of 46, co-authored 202 publications receiving 7799 citations. Previous affiliations of Levi J. Hargrove include Rehabilitation Institute of Chicago & University of New Brunswick.
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Journal ArticleDOI
A Comparison of Surface and Intramuscular Myoelectric Signal Classification
TL;DR: This paper compares the classification accuracy of six pattern recognition-based myoelectric controllers which use multi-channel surface MES as inputs to the same controllers which UseMulti-channel intramuscular M ES as inputs.
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Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion
TL;DR: An algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations outperformed methods that used only EMG signals or mechanical information.
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Determining the Optimal Window Length for Pattern Recognition-Based Myoelectric Control: Balancing the Competing Effects of Classification Error and Controller Delay
TL;DR: Both classification error and controller delay should be considered when choosing a window length; it may be beneficial to increase the window length if this results in a reduced classification error, despite the corresponding increase in controller delay.
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Classification of Simultaneous Movements Using Surface EMG Pattern Recognition
TL;DR: The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements.
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A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control
TL;DR: The results show that electrode displacements adversely affect classification accuracy, but training the system to recognize plausible displacement locations mitigates the effect.