Sensor Selection With Composite Features in Identifying User-Intended Poses for Human-Prosthetic Interfaces
Author:
Affiliation:
1. Department of Mechanical Engineering, University of Melbourne, Parkville, VIC, Australia
2. Department of Surgery, St. Vincent’s Hospital Melbourne, University of Melbourne, Fitzroy, VIC, Australia
Funder
Valma Angliss Trust
University of Melbourne
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation
Link
http://xplorestaging.ieee.org/ielx7/7333/10031624/10073539.pdf?arnumber=10073539
Reference42 articles.
1. Channel selection for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom
2. Feature reduction and selection for EMG signal classification
3. Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis
4. Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol
5. On the Time-Invariance Properties of Upper Limb Synergies
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1. Discrete-Target Prosthesis Control Using Uncertainty-Aware Classification for Smooth and Efficient Gross Arm Movement;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024
2. Data-Driven Goal Recognition in Transhumeral Prostheses Using Process Mining Techniques;2023 5th International Conference on Process Mining (ICPM);2023-10-23
3. Feasibility Evaluation of Online Classification-based Control for Gross Movement in a 2-DoF Prosthetic Arm;2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2023-07-24
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