Advances in Physical Therapy and Rehabilitation, 2nd Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 618

Special Issue Editors


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Guest Editor
Doctor of Physical Therapy Program, College of Health Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
Interests: clinical applied physiology; cardiovascular pathophysiology and rehabilitation; exercise-induced blood flow patterns; endothelial function
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Guest Editor
Department of Physical Therapy and Movement Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
Interests: stroke rehabilitation; wearable sensors; telerehabilitation; neuroplasticity

Special Issue Information

Dear Colleagues,

This Special Issue is the second edition of “Advances in Physical Therapy and Rehabilitation” (https://www.mdpi.com/journal/bioengineering/special_issues/6KL66M0LED).

In the past few years, the field of physical therapy and rehabilitation has benefitted from close collaboration with engineers to enhance patients’ outcomes and quality of life. From 3D printing for adaptive devices and hearing aids to exoskeletons and virtual reality, bioengineers have provided important support to the rehabilitation field in general. As these two fields, rehabilitation and engineering, have produced brilliant work in the past, I look forward to seeing what the present and future have to offer.

This Special Issue will focus on current research advancing any collaborations between rehabilitation and engineering, from a biological perspective, via basic and applied sciences research, to environmental and mechanical factors that play key roles in the recovery of our patients. We welcome manuscripts covering a broad overview of bioengineering research topics to enhance the physical therapy and rehabilitation field. These topics might be related to basic or applied sciences, such as biomechanics, physiology, motor control, or any other factor associated with bioengineering in rehabilitation.

Dr. Alvaro N. Gurovich
Dr. Shashwati Geed
Guest Editors

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Keywords

  • biomedical engineering
  • rehabilitation engineering
  • biomechanics
  • motor control
  • applied physiology
  • 3D printing
  • additive manufacture
  • imaging
  • ultrasound

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Published Papers (1 paper)

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Research

22 pages, 7878 KB  
Article
Toward Sensor-to-Text Generation: Leveraging LLM-Based Video Annotations for Stroke Therapy Monitoring
by Mohammad Akidul Hoque, Shamim Ehsan, Anuradha Choudhury, Peter Lum, Monika Akbar, Shashwati Geed and M. Shahriar Hossain
Bioengineering 2025, 12(9), 922; https://doi.org/10.3390/bioengineering12090922 - 27 Aug 2025
Viewed by 460
Abstract
Stroke-related impairment remains a leading cause of long-term disability, limiting individuals’ ability to perform daily activities. While wearable sensors offer scalable monitoring solutions during rehabilitation, they struggle to distinguish functional from non-functional movements, and manual annotation of sensor data is labor-intensive and prone [...] Read more.
Stroke-related impairment remains a leading cause of long-term disability, limiting individuals’ ability to perform daily activities. While wearable sensors offer scalable monitoring solutions during rehabilitation, they struggle to distinguish functional from non-functional movements, and manual annotation of sensor data is labor-intensive and prone to inconsistency. In this paper, we propose a novel framework that uses large language models (LLMs) to generate activity descriptions from video frames of therapy sessions. These descriptions are aligned with concurrently recorded accelerometer signals to create labeled training data. Through exploratory analysis, we demonstrate that accelerometer signals exhibit distinct temporal and statistical patterns corresponding to specific activities, supporting the feasibility of generating natural language narratives directly from sensor data. Our findings lay the foundation for future development of sensor-to-text models that can enable automated, non-intrusive, and scalable stroke rehabilitation monitoring without the need for manual or video-based annotation. Full article
(This article belongs to the Special Issue Advances in Physical Therapy and Rehabilitation, 2nd Edition)
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