Bioinspired Smart Triboelectric Soft Pneumatic Actuator-Enabled Hand Rehabilitation Robot.

Quantitative assessment for post-stroke spasticity remains a significant challenge due to the encountered variable resistance during passive stretching, which can lead to the widely used modified Ashworth scale (MAS) for spasticity assessment depending heavily on rehabilitation physicians. To address these challenges, a high-force-output triboelectric soft pneumatic actuator (TENG-SPA) inspired by a lobster tail is developed. The bioinspired TENG-SPA can generate approximately 20 N at 0.

Triboelectric-Inertial Sensing Glove Enhanced by Charge-Retained Strategy for Human-Machine Interaction.

As technology advances, human-machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric-based sensors, with their self-powered, cost-effective, and material various characteristics, can offer promising solutions for enhancing existing glove systems.

Cortical Activation Patterns Determine Effectiveness of rTMS-induced Motor Imagery Decoding Enhancement in Stroke Patients.

Combination therapy with motor imagery (MI)-based brain-computer interface (BCI) and repetitive transcranial magnetic stimulation (rTMS) is a promising therapy for poststroke neurorehabilitation. However, with patients' individual differences, the clinical effects vary greatly. This study aims to explore the hypothesis that stroke patients show individualized cortical response to rTMS treatments, which determine the effectiveness of rTMS-induced MI decoding enhancement.

Enhancing Brain-Computer Interface Performance by Incorporating Brain-to-Brain Coupling.

Human cooperation relies on key features of social interaction in order to reach desirable outcomes. Similarly, human-robot interaction may benefit from integration with human-human interaction factors. In this paper, we aim to investigate brain-to-brain coupling during motor imagery (MI)-based brain-computer interface (BCI) training using eye-contact and hand-touch interaction.

Tetramethylpyrazine-loaded electroconductive hydrogels promote tissue repair after spinal cord injury by protecting the blood-spinal cord barrier and neurons.

Spinal cord injury (SCI) usually induces profound microvascular dysfunction. It disrupts the integrity of the blood-spinal cord barrier (BSCB), which could trigger a cascade of secondary pathological events that manifest as neuronal apoptosis and axonal demyelination. These events can further lead to irreversible neurological impairments.

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings.

Influence of Amputation on Kinetic Chain Musculature Activity During Basic and Modified Core Exercises.

Purpose: Core strength is vital for athletic performance, and many more exercises that involve the kinetic chain have been designed for able-bodied athletes. Disabilities that impair the kinetic chain can reduce the effectiveness of strength training. However, the impact of amputation on core strength training of people with disabilities and its underlying mechanism remains unclear.

Multiscale Canonical Coherence for Functional Corticomuscular Coupling Analysis.

Functional corticomuscular coupling (FCMC) probes multi-level information communication in the sensorimotor system. The canonical Coherence (caCOH) method has been leveraged to measure the FCMC between two multivariate signals within the single scale. In this paper, we propose the concept of multiscale canonical Coherence (MS-caCOH) to disentangle complex multi-layer information and extract coupling features in multivariate spaces from multiple scales.

Motor-Respiratory Coupling Improves Endurance Performance during Rhythmic Isometric Handgrip Exercise.

Purpose: This study aimed to evaluate whether motor-respiratory coupling exists in rhythmic isometric handgrip exercises and its effect on endurance performance. In addition, the mechanism underlying observed effects was to be investigated if higher motor-respiratory coupling rate could enhance endurance performance.

Methods: Eleven subjects completed three rhythmic isometric handgrip trials to task failure in a randomized manner.

Slackline training for Paralympic alpine sit skiers: Development of human-device multi-joint coordination.

Purpose: Para-alpine sit skiers face unique challenges in balance control due to their disabilities and the use of sit skis. This study assessed their multi-joint coordination before and after slackline training.

Methods: Nine alpine sit skiers (6 M/3 F; 27 ± 8 years; height: 168.

Enhancement of EEG-EMG coupling detection using corticomuscular coherence with spatial-temporal optimization.

. Corticomuscular coherence (CMC) is widely used to detect and quantify the coupling between motor cortex and effector muscles. It is promisingly used in human-machine interaction (HMI) supported rehabilitation training to promote the closed-loop motor control for stroke patients.

EEG-based evaluation of motion sickness and reducing sensory conflict in a simulated autonomous driving environment.

Autonomous driving offers significant potential for changes in the automotive industry. However, sensory conflict during autonomous driving can lead to motion sickness. Quantitative evaluation and effective preventions to predict and reduce motion sickness are needed.

A Transferable Deep Learning Prognosis Model for Predicting Stroke Patients' Recovery in Different Rehabilitation Trainings.

Since the underlying mechanisms of neurorehabilitation are not fully understood, the prognosis of stroke recovery faces significant difficulties. Recovery outcomes can vary when undergoing different treatments; however, few models have been developed to predict patient outcomes toward multiple treatments. In this study, we aimed to investigate the potential of predicting a treatment's outcome using a deep learning prognosis model developed for another treatment.

Application, challenge and perspective of triboelectric nanogenerator as micro-nano energy and self-powered biosystem.

As a new type of energy harvesting technology, the triboelectric nanogenerator (TENG) can convert distributed energy into electrical energy. It is widely used in various fields such as wearable devices, biomedical devices, Internet of Things (IoT), natural environment, etc. However, there are still some issues that need to be solved for the commercial implementation of TENGs.

Tailoring brain-machine interface rehabilitation training based on neural reorganization: towards personalized treatment for stroke patients.

Electroencephalogram (EEG)-based brain-machine interface (BMI) has the potential to enhance rehabilitation training efficiency, but it still remains elusive regarding how to design BMI training for heterogeneous stroke patients with varied neural reorganization. Here, we hypothesize that tailoring BMI training according to different patterns of neural reorganization can contribute to a personalized rehabilitation trajectory. Thirteen stroke patients were recruited in a 2-week personalized BMI training experiment.

Recognizing the individualized sensorimotor loop of stroke patients during BMI-supported rehabilitation training based on brain functional connectivity analysis.

Background: Electroencephalogram (EEG) based brain-machine interaction training can facilitate rehabilitation by closing the sensorimotor loop. However, it remains unclear how to evaluate whether the loop is closed, especially for stroke patients whose brain regions of motor control and sensorimotor feedback could be altered. Our hypothesis is that motor recovery depends on whether sensorimotor loop is established poststroke.

Brain oscillatory activity correlates with the relief of post-stroke spasticity following focal vibration.

Background: Some evidence has demonstrated that focal vibration (FV) contributes to the relief of post-stroke spasticity (PSS). Although the changes of cortical activity correlating with the relief of PSS induced by FV have been explored using transcranial magnetic stimulation, brain oscillatory activity during the above-mentioned process has not been fully understood.

Objective: The main purpose of this study is to explore the correlation between the changes in brain oscillatory activity and the relief of PSS following FV.

Size-Controllable Eu-MOFs through Machine Learning Technology: Application for High Sensitive Ions and Small-Molecular Identification.

Metal-organic frameworks (MOFs) with the aggregation-induced emission (AIE) activities exhibit potential applications in the fields of energy and biomedical technology. However, the controllable synthesis of MOFs in the varied particle sizes not only affects their AIE activities, but also restricts their application scenarios. In this work, the varied particle sizes of Eu-MOFs are synthesized by adjusting the synthesis process parameters, and their variation rules combining the single factor analysis method with machine learning technology are studied.

Decoding lip language using triboelectric sensors with deep learning.

Lip language is an effective method of voice-off communication in daily life for people with vocal cord lesions and laryngeal and lingual injuries without occupying the hands. Collection and interpretation of lip language is challenging. Here, we propose the concept of a novel lip-language decoding system with self-powered, low-cost, contact and flexible triboelectric sensors and a well-trained dilated recurrent neural network model based on prototype learning.

5 Hz rTMS improves motor-imagery based BCI classification performance.

Brain-computer interface (BCI) based rehabilitation has been proven a promising method facilitating motor recovery. Recognizing motor intention is crucial for realizing BCI rehabilitation training. Event-related desynchronization (ERD) is a kind of electroencephalogram (EEG) inherent characteristics associated with motor intention.

CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training.

Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient's neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice.