Background: Mirror therapy (MT) is an intervention used for upper extremity rehabilitation in stroke patients and has been studied in various fields. Recently, effective MT methods have been introduced in combination with neuromuscular electrical stimulation or with electromyography (EMG)-triggered biofeedback. The purpose of this study was to investigate the effects of functional electrical stimulation (FES)-based MT incorporating a motion recognition biofeedback device on upper extremity motor recovery to chronic stroke patients.

Methods: Twenty-six chronic stroke patients with onset of more than 6 months were randomly assigned into experimental group (n = 13) and control group (n = 13). Both groups participated in conventional rehabilitation program, while the control group received conventional MT intervention and the experimental group received FES-based MT with motion recognition biofeedback device. All interventions were conducted for 30 min/d, 5 d/wk, for 4 weeks. Upper limb motor recovery, upper limb function, active-range of motion (ROM), and activities of daily living independence were measured before and after the intervention and compared between the 2 groups.

Results: The Fugl-Meyer assessment (FMA), manual function test (MFT), K-MBI, and active-ROM (excluding deviation) were significantly improved in both groups (P < .05). Only the experimental group showed significant improvement in upper extremity recovery, ulnar and radial deviation (P < .05). There was a significant difference of change in Brunstrom's recovery level, FMA, MFT, and active-ROM in the experimental group compared to the control group (P < .05).

Conclusion: FES-based MT using gesture recognition biofeedback is an effective intervention method for improving upper extremity motor recovery and function, active-ROM in patients with chronic stroke. This study suggests that incorporating gesture-recognition biofeedback into FES-based MT can provide additional benefits to patients with chronic stroke.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10754587PMC
http://dx.doi.org/10.1097/MD.0000000000036546DOI Listing

Publication Analysis

Top Keywords

recognition biofeedback
12
upper extremity
12
chronic stroke
12
functional electrical
8
mirror therapy
8
stroke patients
8
electrical stimulation
8
motion recognition
8
biofeedback device
8
motor recovery
8

Similar Publications

This study addresses the limitations of traditional sports rehabilitation, emphasizing the need for improved accuracy and response speed in real-time action detection and recognition in complex rehabilitation scenarios. We propose the STA-C3DL model, a deep learning framework that integrates 3D Convolutional Neural Networks (C3D), Long Short-Term Memory (LSTM) networks, and spatiotemporal attention mechanisms to capture nuanced action dynamics more precisely. Experimental results on multiple datasets, including NTU RGB + D, Smarthome Rehabilitation, UCF101, and HMDB51, show that the STA-C3DL model significantly outperforms existing methods, achieving up to 96.

View Article and Find Full Text PDF
Article Synopsis
  • The study investigates the use of a real-time, continuous system that manipulates a virtual arm using electromyogram (EMG) signals, unlike previous methods that relied on machine learning or trigger controls for specific motions.
  • An experiment involving seven healthy participants was conducted to assess changes in their motor control strategies after experiencing the physio-avatar EMG biofeedback system, which aims to induce physiological changes through new interactions.
  • Results showed that while participants initially adapted their motor control strategies using the physio-avatar system, they eventually reverted to their original strategies, indicating a significant impact on their motor control but also a tendency to return to baseline after the intervention.
View Article and Find Full Text PDF

Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states.

View Article and Find Full Text PDF

The emulation of tactile sensory nerves to achieve advanced sensory functions in robotics with artificial intelligence is of great interest. However, such devices remain bulky and lack reliable competence to functionalize further synaptic devices with proprioceptive feedback. Here, we report an artificial organic afferent nerve with low operating bias (-0.

View Article and Find Full Text PDF

Upper limb amputation severely affects the quality of life of individuals. Therefore, developing closed-loop upper-limb prostheses would enhance the sensory-motor capabilities of the prosthetic user. Considering design priorities based on user needs, the restoration of sensory feedback is one of the most desired features.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!