J
Jie Liu
Researcher at Rehabilitation Institute of Chicago
Publications - 29
Citations - 2895
Jie Liu is an academic researcher from Rehabilitation Institute of Chicago. The author has contributed to research in topics: Electromyography & Motor unit. The author has an hindex of 16, co-authored 27 publications receiving 2430 citations. Previous affiliations of Jie Liu include Brown University & United States Department of Veterans Affairs.
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Journal ArticleDOI
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Leigh R. Hochberg,Daniel Bacher,Beata Jarosiewicz,Beata Jarosiewicz,Nicolas Y. Masse,John D. Simeral,John D. Simeral,John D. Simeral,Joern Vogel,Sami Haddadin,Jie Liu,Jie Liu,Sydney S. Cash,Patrick van der Smagt,John P. Donoghue,John P. Donoghue +15 more
TL;DR: The results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.
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A Novel Myoelectric Pattern Recognition Strategy for Hand Function Restoration After Incomplete Cervical Spinal Cord Injury
TL;DR: High average overall accuracies were achieved in classification of seven different classes, indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects, thereby facilitating restoration of hand function.
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Power spectral analysis of surface electromyography (EMG) at matched contraction levels of the first dorsal interosseous muscle in stroke survivors.
TL;DR: There appears to be complex muscular and neural processes at work post stroke that may impact the surface EMG power spectrum.
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EMG-Based Real-Time Linear-Nonlinear Cascade Regression Decoding of Shoulder, Elbow, and Wrist Movements in Able-Bodied Persons and Stroke Survivors
TL;DR: The proposed method is capable of simultaneously and continuously estimating multi-joint movements of the human arm in real-time by characterizing the nonlinear mappings between muscle activity and kinematic signals based on linear regression.
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Adaptive myoelectric pattern recognition toward improved multifunctional prosthesis control
TL;DR: The proposed approach is capable of incorporating the useful information in testing data to the classification model by taking into account the overtime changes in the testing data with respect to the training data to retrain the original classifier, therefore providing a self-correction mechanism for suppressing misclassifications.