Background: Stroke is becoming more and more a disease of chronically disabled patients, and new approaches are needed for better outcomes. An intervention based on robot fully assisted upper-limb functional movements is presented.
Objectives: To test the immediate and sustained effects of the intervention in reducing impairment in chronic stroke and to preliminarily verify the effects on activity.
Methodology: Nineteen patients with mild-to-severe impairment underwent 12 40-min rehabilitation sessions, 3 per week, of robot-assisted reaching and hand-to-mouth movements. The primary outcome measure was the Fugl-Meyer Assessment (FMA) at T1, immediately after treatment ( = 19), and at T2, at a 6-month follow-up ( = 10). A subgroup of 11 patients was also administered the Wolf Motor Function Test Time (WMFT TIME) and Functional Ability Scale (WMFT FAS) and Motor Activity Log (MAL) Amount Of Use (AOU), and Quality Of Movement (QOM).
Results: All patients were compliant with the treatment. There was improvement on the FMA with a mean difference with respect to the baseline of 6.2 points at T1, after intervention ( = 19, 95% CI = 4.6-7.8, < 0.0002), and 5.9 points at T2 ( = 10, 95% CI = 3.6-8.2, < 0.005). Significant improvements were found at T1 on the WMFT FAS ( = 11, +0.3/5 points, 95% CI = 0.2-0.4, < 0.004), on the MAL AOU ( = 11, +0.18/5, 95% CI = 0.07-0.29, < 0.02), and the MAL QOM ( = 11, +0.14/5, 95% CI = 0.08-0.20, < 0.02).
Conclusions: Motor benefits were observed immediately after intervention and at a 6-month follow-up. Reduced impairment would appear to translate to increased activity. Although preliminary, the results are encouraging and lay the foundation for future studies to confirm the findings and define the optimal dose-response curve.
Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03208634.
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http://dx.doi.org/10.3389/fneur.2021.782094 | DOI Listing |
Sci Rep
January 2025
School of Aerospace Engineering, Gyeongsang National University, Jinju-si, 52828, Gyeongsangnam-do, Republic of Korea.
This study introduces a novel deep learning-based technique for predicting pressure distribution images, aimed at application in image-based approximate optimal design. The proposed approach integrates both unsupervised and supervised learning paradigms, employing autoencoders (AE) for the unsupervised component and fully connected neural networks (FNN) for the supervised component. A surrogate model based on 2D image data was developed, enabling a comparative analysis of three distinct methods: the conventional AE, the convolutional autoencoder (CAE), and a hybrid CAE, which combines the CAE with a conventional AE.
View Article and Find Full Text PDFHealthc Technol Lett
December 2024
Robotics and Control Laboratory, Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada.
The Segment Anything model (SAM) is a powerful vision foundation model that is revolutionizing the traditional paradigm of segmentation. Despite this, a reliance on prompting each frame and large computational cost limit its usage in robotically assisted surgery. Applications, such as augmented reality guidance, require little user intervention along with efficient inference to be usable clinically.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.
Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.
J Comput Chem
January 2025
Scuola Superiore Meridionale, Napoli, Italy.
Light-driven molecular rotary motors are nanometric machines able to convert light into unidirectional motions. Several types of molecular motors have been developed to better respond to light stimuli, opening new avenues for developing smart materials ranging from nanomedicine to robotics. They have great importance in the scientific research across various disciplines, but a detailed comprehension of the underlying ultrafast photophysics immediately after photo-excitation, that is, Franck-Condon region characterization, is not fully achieved yet.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
College of Mechanical Engineering, Baoji University of Arts and Sciences, Baoji 721016, China.
Accurate analyses of contact problems for rough surfaces are important but complicated. Some assumptions, namely that all asperities can be approximated by a hemisphere with the same radius and assuming a Gaussian distribution of the asperity heights, are convenient but may lead to less accurate results. The purpose of this work is to investigate these assumptions and analyze the conditions under which they are valid.
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