Robert M Hinson
College of Engineering
Works (6)
Updated: March 13th, 2024 05:00
2023 journal article
Ankle Torque Estimation With Motor Unit Discharges in Residual Muscles Following Lower-Limb Amputation
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 31, 4821–4830.
author keywords: Amputation; EMG; motor unit; neural-machine interface; prosthesis control
TL;DR:
Results suggest MUDrive significantly outperforms EMG and ND methods in muscles of NON, as well as both intact and residual muscles of AMP, and integrating MU discharges with modeled biomechanical outputs may provide a more accurate torque control signal than direct EMG control of assistive, lower-limb devices.
(via Semantic Scholar)
open access via ieeexplore.ieee.org (publisher PDF)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: March 11, 2024
2023 journal article
Offline Evaluation Matters: Investigation of the Influence of Offline Performance of EMG-Based Neural-Machine Interfaces on User Adaptation, Cognitive Load, and Physical Efforts in a Real-Time Application
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 31, 3055–3063.
author keywords: EMG decoding; neural machine interface; cognitive load; adaptation
TL;DR:
The results imply that the offline performance of EMG-based NMIs provide important insight to users’ abilities to utilize them and should play an important role in research and development of novel NMI algorithms.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 28, 2023
2022 journal article
Harnessing Machine Learning and Physiological Knowledge for a Novel EMG-Based Neural-Machine Interface
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 70(4), 1125–1136.
author keywords: Electromyography; Decoding; Solid modeling; Electrodes; Artificial neural networks; Muscles; Machine learning; Artificial neural network; electromyography (EMG); musculoskeletal model; neural machine interface; reinforcement learning
MeSH headings : Electromyography / methods; Hand / physiology; Upper Extremity; Posture; Machine Learning
TL;DR:
Combining the concepts of machine learning and musculoskeletal modeling has resulted in a more robust joint kinematics decoder than either concept individually, which may result in a novel, highly reliable controller for powered prosthetic hands.
(via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 15, 2023
2022 journal article
Offline Evaluation Matters: Investigation of the Influence of Offline Performance on Real-Time Operation of Electromyography-Based Neural-Machine Interfaces
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 31, 680–689.
Contributors: n, J. Berman n, W. Filer *, D. Kamper n, X. Hu n & H. Huang n
author keywords: Electromyography; Real-time systems; Decoding; Task analysis; Artificial neural networks; Wrist; Measurement; machine learning; neural-machine interface; offline analysis; real-time performance
TL;DR:
The use and validity of offline analyses for optimization of NMIs in myoelectric control research and development are supported and significant improvements in each real-time task evaluation metric were observed.
(via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 15, 2023
2022 journal article
Ultrasoft Porous 3D Conductive Dry Electrodes for Electrophysiological Sensing and Myoelectric Control
Advanced Materials Technologies, 7(10).
author keywords: biopotential; dry electrodes; electrocardiogram; electroencephalogram; electromyogram; nanomaterials
TL;DR:
Based on the muscle activities captured by the electrodes and a musculoskeletal model, electromyogram‐based neural–machine interfaces are realized, illustrating the great potential for prosthesis control, neurorehabilitation, and virtual reality.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Sources: Web Of Science, NC State University Libraries, ORCID, Crossref
Added: May 31, 2022
2021 article
Comparing Reinforcement Learning Agents and Supervised Learning Neural Networks for EMG-Based Decoding of Continuous Movements
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), pp. 6297–6300.
MeSH headings : Electromyography; Humans; Movement; Neural Networks, Computer; Supervised Machine Learning; Wrist Joint
TL;DR:
This study aimed to present a new kinematics decoding framework based on reinforcement learning (RL), which combines machine learning and model-based approaches together and suggests that the RL-based kinematic decoder is more robust to changes in movement speeds between training and testing data and has better performance than the SLNN.
(via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: January 7, 2022
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