deep learning

Early Detection of Parkinson’s Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings
Early Detection of Parkinson’s Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
With the development of digital medical technology, ubiquitous smartphones are emerging as valuable tools for the detection of complex and elusive diseases. This paper exploits smartphone walking recording for early… read more

MEFFNet: Forecasting Myoelectric Indices of Muscle Fatigue in Healthy and Post-Stroke During Voluntary and FES-Induced Dynamic Contractions
MEFFNet: Forecasting Myoelectric Indices of Muscle Fatigue in Healthy and Post-Stroke During Voluntary and FES-Induced Dynamic Contractions 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Myoelectric indices forecasting is important for muscle fatigue monitoring in wearable technologies, adaptive control of assistive devices like exoskeletons and prostheses, functional electrical stimulation (FES)-based Neuroprostheses, and more. Non-stationary temporal… read more

EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset
EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet… read more

EMG-based Multi-User Hand Gesture Classification via Unsupervised Transfer Learning Using Unknown Calibration Gestures
EMG-based Multi-User Hand Gesture Classification via Unsupervised Transfer Learning Using Unknown Calibration Gestures 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
The poor generalization performance and heavy training burden of the gesture classification model contribute as two main barriers that hinder the commercialization of sEMG-based human-machine interaction (HMI) systems. To overcome… read more

Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation
Author(s)3: Guangyi Zhang, Ali Etemad
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to…

read more

Deep-Learning-Based Emergency Stop Prediction for Robotic Lower-Limb Rehabilitation Training Systems
Author(s)3: Baekdong Cha, Kyung-hwan Lee, Jeha Ryu
Deep-Learning-Based Emergency Stop Prediction for Robotic Lower-Limb Rehabilitation Training Systems 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Robotic lower-limb rehabilitation training is a better alternative for the physical training efforts of a therapist due to advantages, such as intensive repetitive motions, economical therapy, and quantitative assessment of…

read more

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring
Author(s)3: Aleš Procházka, Ondřej Dostál, Pavel Cejnar, Hagar Ibrahim Mohamed, Zbyšek Pavelek, Martin Vališ, Oldřich Vyšata
Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring 526 336 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibility…

read more

Improved High-density Myoelectric Pattern Recognition Control Against Electrode Shift Using Data Augmentation and Dilated Convolutional Neural NetworkT
Author(s)3: Le Wu, Xu Zhang, Kun Wang, Xiang Chen, Xun Chen
Improved High-density Myoelectric Pattern Recognition Control Against Electrode Shift Using Data Augmentation and Dilated Convolutional Neural NetworkT 553 217 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
The objective of this work is to develop a robust method for myoelectric control towards alleviating the in-terference of electrode shift. Methods: In the proposed method, a preprocessing approach was first performed to convert high-den-sity surface electromyogram (HD-sEMG) signals into a series of images, and the electrode shift appeared as pixel shift in these im-ages. read more

Deep Learning Architecture to Assist with Steering a Powered Wheelchair
Author(s)3: Malik J. Haddad, David A. Sanders
Deep Learning Architecture to Assist with Steering a Powered Wheelchair 582 322 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
This paper describes a novel Deep Learning architecture to assist with steering a powered wheelchair. A rule-based approach is utilized to train and test a Long Short Term Memory (LSTM) Neural Network. It is the first time a LSTM has been used for steering a powered wheelchair. read more

Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach
Author(s)3: Guangyu Jia, Hak-Keung Lam, Shichao Ma, Zhaohui Yang, Yujia Xu, Bo Xiao
Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach 874 501 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces…

read more
Search this website