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Smart Wires and Modular FACTS Controllers for Smart Grid Applications: A Review
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Prediction of Energy Consumption by Ships at the port using Deep Learning
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A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
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Power Transfer Capability Recognition in Deregulated System under Line Outage Condition Using Power World Simulator
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Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model
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Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
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Performance Analysis of Multiple Pico Hydro Power Generation
Volume-2 | Issue-2
Energy Efficient Data Mining Approach for Estimating the Diabetes
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Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
Volume-3 | Issue-3
Prediction of Energy Consumption by Ships at the port using Deep Learning
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A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
Volume-3 | Issue-4
Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
Volume-3 | Issue-2
Volume - 4 | Issue - 4 | december 2022
Published
29 November, 2022
The work presented in this paper addresses the enhancement of upper body rehabilitation and training methods for stroke victims and upper body amputees. One of the primary aims is to develop a tool that utilizes augmented reality to facilitate the rehabilitation of impaired human hand and forearm movements by employing mirror neurons and virtual reality. The second objective of the proposed tool is to allow for evaluation and specification of prostheses prior to acquisition and fitting of such devices to upper limb amputees. The proposed system involves the development of real–time surface Electromyography (sEMG) signal classification methods, Artificial Neural Network training, and implementation and the development of identification algorithms for inferring motion intend. The results of the proposed approach indicate preferences of specific classifiers used in the processing of sEMG data. The proposed methods are implemented in a virtual reality environment allowing for potential selection and training of prosthetic device usage as well as for physical therapy rehabilitation sessions of stroke victims.
KeywordsArtificial neural network motion identification rehabilitation prosthesis hand real – time model surface electromyography classification virtual reality
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