Electromyography

: Learning a Hand Model from Dynamic Movements Using High-Density EMG and Convolutional Neural Networks

Learning a Hand Model from Dynamic Movements Using High-Density EMG and Convolutional Neural Networks
Learning a Hand Model from Dynamic Movements Using High-Density EMG and Convolutional Neural Networks 750 422 IEEE Transactions on Biomedical Engineering (TBME)
Deep learning model decodes surface electromyographic signals into proportional hand movements, accurately controlling both individual finger and compound movements, with potential to enhance intuitive interfaces for assistive hand devices. read more
Emulator-Based Optimization of a Semi-Active Hip Exoskeleton Concept: Sweeping Impedance Across Walking Speeds

Emulator-Based Optimization of a Semi-Active Hip Exoskeleton Concept: Sweeping Impedance Across Walking Speeds
Emulator-Based Optimization of a Semi-Active Hip Exoskeleton Concept: Sweeping Impedance Across Walking Speeds 782 430 IEEE Transactions on Biomedical Engineering (TBME)
This is the first study to optimize semi-active hip exoskeleton assistance across walking speeds to reduce metabolic cost. We found personalization is important and online tuning can focus on minimizing local muscle activity. read more
An EMG-Assisted Muscle-Force Driven Finite Element Analysis Pipeline to Investigate Joint- and Tissue-Level Mechanical Responses in Functional Activities: Towards a Rapid Assessment Toolbox

An EMG-Assisted Muscle-Force Driven Finite Element Analysis Pipeline to Investigate Joint- and Tissue-Level Mechanical Responses in Functional Activities: Towards a Rapid Assessment Toolbox
An EMG-Assisted Muscle-Force Driven Finite Element Analysis Pipeline to Investigate Joint- and Tissue-Level Mechanical Responses in Functional Activities: Towards a Rapid Assessment Toolbox 788 444 IEEE Transactions on Biomedical Engineering (TBME)
This study established an electromyography-assisted neuromusculoskeletal rigid-body and finite element modeling pipeline enabled for large cohorts studies, encouraging further research to investigate the pipeline’s potential for personalized treatment-planning of knee osteoarthritis. read more

Muscle-Specific High-Density Electromyography Arrays for Hand Gesture Classification
Author(s)3: Leo K. Cheng
Muscle-Specific High-Density Electromyography Arrays for Hand Gesture Classification IEEE Transactions on Biomedical Engineering (TBME)
Muscle-specific, high-density, flexible electromyography (HD-EMG) electrode arrays were designed and applied to capture the myoelectric activity of key intrinsic hand muscles to classify motions and to allow individual analysis of each muscle. Myoelectric activity was displayed as spatio-temporal maps to visualize muscle activation. Time-domain and temporal-spatial HD-EMG features were extracted to train machine machine-learning classifiers to predict user motion, using data collected from intrinsic hand muscles. The muscle-specific electrode arrays can be combined with EMG decomposition techniques to assess motor unit activity and in applications involving the analysis of dexterous hand motions. read more

Intramuscular EMG-driven Musculoskeletal Modelling: Towards Implanted Muscle Interfacing in Spinal Cord Injury Patients
Author(s)3: Moon Ki Jung, Silvia Muceli, Camila Rodrigues, Álvaro Megía-García, Alejandro Pascual-Valdunciel, Antonio J. del-Ama, Angel Gil-Agudo, Juan C. Moreno, Filipe Oliveira Barroso, José L. Pons, Dario Farina
Intramuscular EMG-driven Musculoskeletal Modelling: Towards Implanted Muscle Interfacing in Spinal Cord Injury Patients 605 605 IEEE Transactions on Biomedical Engineering (TBME)
EMG-driven neuromusculoskeletal (NMS) modelling approaches have been developed to estimate user-intended joint moments. This study proposes intramuscular EMG-driven NMS modelling as a control method applied to recordings from muscle implants with the long-term goal of applications in assistive exoskeletons for spinal cord injury (SCI) patients. We recorded intramuscular EMG (iEMG) and provided joint torque predictions based on the NMS model. The approach was applied to healthy individuals as well as incomplete SCI patients. The results showed high correlation between experimental and predicted joint torques as well as comparable performance when using non-invasive and implanted EMG systems. read more

Design and Preliminary Assessment of a Passive Elastic Leg Exoskeleton for Resistive Gait Rehabilitation
Author(s)3: Edward Peter Washabaugh, Thomas Edmund Augenstein, Alissa Marie Ebenhoeh, Jiajie Qiu, Kaitlyn Ann Ford, Chandramouli Krishnan
Design and Preliminary Assessment of a Passive Elastic Leg Exoskeleton for Resistive Gait Rehabilitation 170 177 IEEE Transactions on Biomedical Engineering (TBME)
While devices that assist walking can enable individuals with neuromusculoskeletal impairments, recovery is often better facilitated by devices that resist walking. Moreover, current robotic interfaces for gait rehabilitation are typically huge, bulky, and come with a large price tag. This article describes the design and development of a novel, wearable, passive elastic exoskeleton for resistive gait rehabilitation. The system uses counteracting compressional springs, pulleys, and clutches and can be configured to resist flexion, extension, or bidirectionally. Thus, the device can target user-specific muscle weaknesses and accommodate range of motion limitations. These concepts were validated using benchtop and human subject experiments. read more

Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement with Temporal Convolutional Networks
Author(s)3: Joseph L. Betthauser, John T. Krall, Shain G. Bannowsky, Gyorgy Levay, Rahul R. Kaliki, Matthew S. Fifer, Nitish V. Thakor
Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement with Temporal Convolutional Networks 170 177 IEEE Transactions on Biomedical Engineering (TBME)
Movement prediction from EMG can be performed by compressing a short window of EMG into a feature-encoding that is meaningful for classification— an approach that can cause erratic prediction behavior. Temporal convolutional networks (TCN) leverage temporal information from EMG to achieve superior predictions for 3 simultaneous degrees-of-freedom that are more accurate and stable, have a very low response delay, and allow for novel types of interactive training. Addressing EMG decoding as a sequential prediction problem requires a new set of considerations that will lead to enhancements in the reliability, responsiveness, and movement complexity available from prosthesis control systems. read more

Faster Gait Speeds Reduce Alpha and Beta EEG Spectral Power From Human Sensorimotor Cortex
Author(s)3: Daniel P. Ferris, W. David Hairston, Andrew D. Nordin
Faster Gait Speeds Reduce Alpha and Beta EEG Spectral Power From Human Sensorimotor Cortex 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Understanding how the human brain controls locomotion is a considerable neuroscience challenge that has required advancements in mobile neuroimaging methods. Mobile high-density electroencephalography (EEG) allows electrical brain activity to be…

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Electrical Stimulation of the Human Cerebral Cortex by Extracranial Muscle Activity: Effect Quantification with Intracranial EEG and FEM Simulations
Author(s)3: Lukas Dominique Josef Fiederer, Jacob Lahr, Johannes Vorwerk, Felix Lucka, Ad Aertsen, Carsten Hermann Wolters, Andreas Sculze-Bonhage, Tonio Ball
Electrical Stimulation of the Human Cerebral Cortex by Extracranial Muscle Activity: Effect Quantification with Intracranial EEG and FEM Simulations 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Lukas D.J. Fiederer, Jacob Lahr, Johannes Vorwerk, Felix Lucka, Ad Aertsen, Carsten H. Wolters, Andreas Schulze-Bonhage, Tonio Ball, University of Freiburg, Germany, University of Münster, Germany, University of Utah, USA, University College…

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Gait Characteristics When Walking on Different Slippery Walkways
Author(s)3: Mariah W. Whitmore, Levi Hargrove, Eric J. Perreault
Gait Characteristics When Walking on Different Slippery Walkways 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Mariah W. Whitmore, Levi J. Hargrove, Eric J. Perreault, Northwestern University, Rehabilitation Institute of Chicago, USA The ability to change gait patterns in the presence of a slippery surface is essential…

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