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Digital Health Technologies for Rehabilitation and Physical Therapy
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Digital Health Technologies for Rehabilitation and Physical Therapy

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 874

Special Issue Editors

Dr. Samuel Stuart

E-Mail Website
Guest Editor
1. Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, UK
2. Department of Neurology, Oregon Health & Science University, Portland, OR, USA
Interests: gait; fNIRS/EEG; eye tracking; wearables; neurology; physiotherapy
Special Issues, Collections and Topics in MDPI journals
Dr. Rosie Morris

E-Mail Website
Guest Editor
Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, UK
Interests: gait; cognition; balance; wearables; neurology; physiotherapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital Health Technologies (DHTs) are changing the way that clinical assessment and rehabilitation are conducted, adding more tools to the therapeutic toolkit. Therefore, it is vital that clinicians have valid and reliable DHTs for rehabilitation to provide high-quality healthcare that is effective and efficient. DHTs come in many forms, including wearable sensors and mobile smart devices or applications which require analytical and clinical validation specific to the context of use (i.e., the clinical population and specific interventions).

This Special Issue will accept submissions of high quality in the topic area of DHTs for rehabilitation or physical therapy.

Topic areas of interest include, but are not limited to, the following:

  • Validation and use of DHTs for relevant outcome measurement to inform rehabilitation interventions.
  • Novel artificial intelligence or machine learning techniques to process DHT data to inform rehabilitation.
  • DHTs of interest may include non-contact, mobile, or wearable sensors, including radar/radio wave sensors, video or image analysis, smart phone/tablet applications, accelerometers/gyroscopes, eye trackers, electroencephalograms, functional near-infrared spectroscopy, etc.
  • Digital therapeutics, such as mobile Health (mHealth), Telehealth, digital cueing, exergaming, virtual reality, augmented reality, or other interventions.

Dr. Samuel Stuart
Dr. Rosie Morris
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital health technology
  • digital therapeutics
  • rehabilitation
  • physical therapy
  • mobility
  • balance
  • wearables
  • smartphones
  • assistive technology
  • mHealth

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Published Papers (1 paper)

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Research

17 pages, 2220 KiB  
Article
Gait Parameters Can Be Derived Reliably and Validly from Augmented Reality Glasses in People with Parkinson’s Disease Performing 10-m Walk Tests at Comfortable and Fast Speeds
by Pieter F. van Doorn, Daphne J. Geerse, Jara S. van Bergem, Eva M. Hoogendoorn, Edward Nyman, Jr. and Melvyn Roerdink
Sensors 2025, 25(4), 1230; https://doi.org/10.3390/s25041230 - 18 Feb 2025
Viewed by 640
Abstract
The 10-m walk test (10MWT) is a stopwatch-based clinical mobility assessment. To better understand mobility limitations, 10MWT test completion times may be complemented with gait parameters like step length. State-of-the-art augmented reality (AR) glasses can potentially do this given their unique 3D-positional data [...] Read more.
The 10-m walk test (10MWT) is a stopwatch-based clinical mobility assessment. To better understand mobility limitations, 10MWT test completion times may be complemented with gait parameters like step length. State-of-the-art augmented reality (AR) glasses can potentially do this given their unique 3D-positional data from which gait parameters may be derived. We examined the test-retest reliability, concurrent validity, and face validity of gait parameters derived from AR glasses during a 10MWT in 20 people with Parkinson’s disease, performed at self-selected comfortable and fast-but-safe walking speeds. AR-derived 10MWT completion times and gait parameters (mean step length, cadence, and maximal gait speed) were compared across repetitions and with lab-based (Interactive Walkway) and clinical (stopwatch) reference systems. Good-to-excellent test-retest reliability statistics were observed for test completion times and gait parameters for all systems and conditions alike. Concurrent validity was demonstrated between AR, lab-based, and clinical references for test completion times (good-to-excellent agreement: ICC > 0.879) and gait parameters (excellent agreement: ICC > 0.942). Face validity was confirmed by significant differences in test completion times and gait parameters between speed conditions in a-priori expected directions. These findings support the conclusion that gait parameters can be derived reliably and validly from AR glasses in people with Parkinson’s disease. Full article
(This article belongs to the Special Issue Digital Health Technologies for Rehabilitation and Physical Therapy)
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