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Temporal Features of Muscle Synergies in Sit-to-Stand Motion Reflect the Motor Impairment of Post-Stroke Patients
Sit-to-stand (STS) motion is an important daily activity, and many post-stroke patients have difficulty performing STS motion. Previous studies found that there are four muscle synergies (synchronized muscle activations) in the STS motion of healthy adults. However, for post-stroke patients, it is unclear whether muscle synergies change and which features primarily reflect motor impairment. Here, we use a machine learning method to demonstrate that temporal features in two muscle synergies that contribute to hip rising and balance maintenance motion reflect the motor impairment of post-stroke patients. Analyzing the muscle...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 30, 2019 Category: Neuroscience Source Type: research

Quantitative Assessment of Upper-Limb Motor Function for Post-Stroke Rehabilitation Based on Motor Synergy Analysis and Multi-Modality Fusion
Functional assessment is an essential part of rehabilitation protocols after stroke. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. In order to objectively quantify the upper-limb motor impairments in patients with post-stroke hemiparesis, this study proposes a novel assessment approach based on motor synergy quantification and multi-modality fusion. Fifteen post-stroke hemiparetic patients and fifteen age-matched healthy persons participated in this study. During different goal-directed tasks, kinematic data and surface electromyography(sEMG) signals were sync...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 31, 2020 Category: Neuroscience Source Type: research

Feasibility of Wearable Sensing for In-Home Finger Rehabilitation Early After Stroke
Wearable grip sensing shows potential for hand rehabilitation, but few studies have studied feasibility early after stroke. Here, we studied a wearable grip sensor integrated with a musical computer game (MusicGlove). Among the stroke patients admitted to a hospital without limiting complications, 13% had adequate hand function for system use. Eleven subjects used MusicGlove at home over three weeks with a goal of nine hours of use. On average they achieved 4.1 ± 3.2 (SD) hours of use and completed 8627 ± 7500 grips, an amount comparable to users in the chronic phase of stroke measured in a previous st...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - May 31, 2020 Category: Neuroscience Source Type: research

An Intelligent Rehabilitation Assessment Method for Stroke Patients Based on Lower Limb Exoskeleton Robot
The 6-min walk distance (6MWD) and the Fugl-Meyer assessment lower-limb subscale (FMA-LE) of the stroke patients provide the critical evaluation standards for the effect of training and guidance of the training programs. However, gait assessment for stroke patients typically relies on manual observation and table scoring, which raises concerns about wasted manpower and subjective observation results. To address this issue, this paper proposes an intelligent rehabilitation assessment method (IRAM) for rehabilitation assessment of the stroke patients based on sensor data of the lower limb exoskeleton robot. Firstly, the feat...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - August 4, 2023 Category: Neuroscience Source Type: research

Robot Training With Vector Fields Based on Stroke Survivors’ Individual Movement Statistics
The wide variation in upper extremity motor impairments among stroke survivors necessitates more intelligent methods of customized therapy. However, current strategies for characterizing individual motor impairments are limited by the use of traditional clinical assessments (e.g., Fugl-Meyer) and simple engineering metrics (e.g., goal-directed performance). Our overall approach is to statistically identify the range of volitional movement capabilities, and then apply a robot-applied force vector field intervention that encourages under-expressed movements. We investigated whether explorative training with such customized f...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - February 1, 2018 Category: Neuroscience Source Type: research

Quantification of Motor Function Post-Stroke Using Novel Combination of Wearable Inertial and Mechanomyographic Sensors
Subjective clinical rating scales represent the gold-standard for diagnosis of motor function following stroke. In practice however, they suffer from well-recognized limitations including assessor variance, low inter-rater reliability and low resolution. Automated systems have been proposed for empirical quantification but have not significantly impacted clinical practice. We address translational challenges in this arena through: (1) implementation of a novel sensor suite combining inertial measurement and mechanomyography (MMG) to quantify hand and wrist motor function; and (2) introduction of a new range of signal featu...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - June 25, 2021 Category: Neuroscience Source Type: research

Learning EEG Representations With Weighted Convolutional Siamese Network: A Large Multi-Session Post-Stroke Rehabilitation Study
Although brain-computer interface (BCI) shows promising prospects to help post-stroke patients recover their motor function, its decoding accuracy is still highly dependent on feature extraction methods. Most current feature extractors in BCI are classification-based methods, yet very few works from literature use metric learning based methods to learn representations for BCI. To circumvent this shortage, we propose a deep metric learning based method, Weighted Convolutional Siamese Network (WCSN) to learn representations from electroencephalogram (EEG) signal. This approach can enhance the decoding accuracy by learning a ...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - October 21, 2022 Category: Neuroscience Source Type: research

Iterative Adjustment of Stimulation Timing and Intensity During FES-Assisted Treadmill Walking for Patients After Stroke
Functional electric stimulation (FES) is a common intervention to correct foot drop for patients after stroke. Due to the disturbances from internal time-varying muscle characteristics under electrical stimulation and external environmental uncertainties, most of the existing FES system used pre-set stimulation parameters and cannot achieve good gait performances during FES-assisted walking. Therefore, an adaptive FES control system, which used the iterative learning control to adjust the stimulation intensity based on kinematic data and a linear model to modulate the stimulation timing based on walking speed during FES-as...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - May 31, 2020 Category: Neuroscience Source Type: research

Classification of Left-Versus Right-Hand Motor Imagery in Stroke Patients Using Supplementary Data Generated by CycleGAN
This study proposes a surrogate EEG data-generation system based on cycle-consistent adversarial networks (CycleGAN) that can expand the number of training data. This study used EEG2Image based on a modified S-transform (MST) to convert EEG data into EEG-topography. This method retains the frequency-domain characteristics and spatial information of the EEG signals. Then, the CycleGAN is used to learn and generate motor-imagery EEG data of stroke patients. From the visual inspection, there is no difference between the EEG topographies of the generated and original EEG data collected from the stroke patients. Finally, we use...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - November 26, 2021 Category: Neuroscience Source Type: research

Monitoring Arm Movements Post-Stroke for Applications in Rehabilitation and Home Settings
Optimal recovery of arm function following stroke requires patients to perform a large number of functional arm movements in clinical therapy sessions, as well as at home. Technology to monitor adherence to this activity would be helpful to patients and clinicians. Current approaches to monitoring arm movements are limited because of challenges in distinguishing between functional and non-functional movements. Here, we present an Arm Rehabilitation Monitor (ARM), a device intended to make such measurements in an unobtrusive manner. The ARM device is based on a single Inertial Measurement Unit (IMU) worn on the wrist and us...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - September 2, 2022 Category: Neuroscience Source Type: research

A Novel Model to Generate Heterogeneous and Realistic Time-Series Data for Post-Stroke Rehabilitation Assessment
The application of machine learning-based tele-rehabilitation faces the challenge of limited availability of data. To overcome this challenge, data augmentation techniques are commonly employed to generate synthetic data that reflect the configurations of real data. One such promising data augmentation technique is the Generative Adversarial Network (GAN). However, GANs have been found to suffer from mode collapse, a common issue where the generated data fails to capture all the relevant information from the original dataset. In this paper, we aim to address the problem of mode collapse in GAN-based data augmentation techn...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - June 20, 2023 Category: Neuroscience Source Type: research

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review
This article reviews seminal works from 2013 onwards, qualitatively and quantitatively adapting the PRISMA approach to examine the potential of robot-assisted, virtual reality-based rehabilitation and automated assessments through data-driven learning. Extensive experimentation on KIMORE and UI-PRMD datasets reveal high agreement between automated methods and therapists. Our investigation shows that deep learning with spatio-temporal skeleton data and dynamic attention outperforms others, with an RMSE as low as 0.55. Fully automated rehabilitation is still in development, but, being an active research topic, it could haste...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 31, 2023 Category: Neuroscience Source Type: research

Learning Post-Stroke Gait Training Strategies by Modeling Patient-Therapist Interaction
For safe and effective robot-aided gait training, it is essential to incorporate the knowledge and expertise of physical therapists. Toward this goal, we directly learn from physical therapists’ demonstrations of manual gait assistance in stroke rehabilitation. Lower-limb kinematics of patients and assistive force applied by therapists to the patient’s leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist’s strategies in response to unique gait behaviors found within a patient’s gait. Prelimi...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - March 23, 2023 Category: Neuroscience Source Type: research

AI Therapist Realizing Expert Verbal Cues for Effective Robot-Assisted Gait Training
Repetitive and specific verbal cues by a therapist are essential in aiding a patient’s motivation and improving the motor learning process. The verbal cues comprise various expressions, sentences, volumes, and timings, depending on the therapist’s proficiency. This paper proposes an AI therapist (AI-T) that implements the verbal cues of professional therapists having extensive experience with robot-assisted gait training using the SUBAR for stroke patients. The AI-T was developed using a neuro-fuzzy system, a machine learning technique leveraging the benefits of fuzzy logic and artificial neural networks. The...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - December 1, 2020 Category: Neuroscience Source Type: research

Automated Evaluation of Upper-Limb Motor Function Impairment Using Fugl-Meyer Assessment
The Fugl-Meyer assessment (FMA) is the most popular instrument for evaluating upper extremity motor function in stroke patients. However, it is a labor-intensive and time-consuming method. This paper proposes a novel automated FMA system to overcome these limitations of the FMA. For automation, we used Kinect v2 and force sensing resistor sensors owing to their convenient installation as compared with body-worn sensors. Based on the linguistic guideline of the FMA, a rule-based binary logic classification algorithm was developed to assign FMA scores using the extracted features obtained from the sensors. The algorithm is a...
Source: IEE Transactions on Neural Systems and Rehabilitation Engineering - January 1, 2018 Category: Neuroscience Source Type: research

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