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Sensors and Wearables for Rehabilitation
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Sensors and Wearables for Rehabilitation

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 3112

Special Issue Editors

Prof. Dr. Natalie Baddour

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, ON K1N 6N5, Canada
Interests: wearable sensors; mHealth; signal processing; mobility
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Edward Lemaire

E-Mail Website
Guest Editor
Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
Interests: mobility; assistive technology; wearable sensors; mHealth; multimedia; disability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Physical rehabilitation is an essential component of the healthcare system, enabling people to move beyond their deficits to achieve their goals, maintain their quality of life, and engage with their community. Understanding a person’s mobility status, mental state, etc., is essential for providing informed and high-quality rehabilitation. Wearable sensors are becoming important tools to achieve this understanding, especially since data can be collected outside the clinic or lab to provide quantitative information about an individual’s chosen environment.

This Special Issue accepts submissions of high-quality research papers and review articles in the topic area of wearable sensor applications for rehabilitation. Submissions must be original contributions that have not been published before and are not currently under review by other journals.

Potential topics of interest include, but are not limited to, the following:

  • Wearable sensors systems for outcome measurement;
  • Machine learning approaches to extract knowledge from wearable sensor systems;
  • Embedded sensor systems in wearable assistive devices (such as prostheses or orthoses);
  • Wearable sensor systems to characterize mental state;
  • Sensor approaches to enhance current clinical outcome measures;
  • Smartphone applications related to rehabilitation, employing sensor or imaging approaches.

Prof. Dr. Natalie Baddour
Prof. Dr. Edward Lemaire
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

  • wearable sensors
  • rehabilitation
  • mobility
  • mHealth
  • assistive technology
  • smartphones
  • machine learning
  • artificial intelligence

Published Papers (4 papers)

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Research

12 pages, 2949 KiB  
Article
The Push Forward in Rehabilitation: Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type
by Rienk van der Slikke, Arie-Willem de Leeuw, Aleid de Rooij and Monique Berger
Sensors 2024, 24(2), 657; https://doi.org/10.3390/s24020657 - 19 Jan 2024
Viewed by 837
Abstract
Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim [...] Read more.
Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation. Full article
(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)
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16 pages, 4267 KiB  
Article
Integrated Quantitative Evaluation of Spatial Cognition and Motor Function with HoloLens Mixed Reality
by Kenya Tada, Yuhei Sorimachi, Kyo Kutsuzawa, Dai Owaki and Mitsuhiro Hayashibe
Sensors 2024, 24(2), 528; https://doi.org/10.3390/s24020528 - 15 Jan 2024
Viewed by 575
Abstract
The steady increase in the aging population worldwide is expected to cause a shortage of doctors and therapists for older people. This demographic shift requires more efficient and automated systems for rehabilitation and physical ability evaluations. Rehabilitation using mixed reality (MR) technology has [...] Read more.
The steady increase in the aging population worldwide is expected to cause a shortage of doctors and therapists for older people. This demographic shift requires more efficient and automated systems for rehabilitation and physical ability evaluations. Rehabilitation using mixed reality (MR) technology has attracted much attention in recent years. MR displays virtual objects on a head-mounted see-through display that overlies the user’s field of vision and allows users to manipulate them as if they exist in reality. However, tasks in previous studies applying MR to rehabilitation have been limited to tasks in which the virtual objects are static and do not interact dynamically with the surrounding environment. Therefore, in this study, we developed an application to evaluate cognitive and motor functions with the aim of realizing a rehabilitation system that is dynamic and has interaction with the surrounding environment using MR technology. The developed application enabled effective evaluation of the user’s spatial cognitive ability, task skillfulness, motor function, and decision-making ability. The results indicate the usefulness and feasibility of MR technology to quantify motor function and spatial cognition both for static and dynamic tasks in rehabilitation. Full article
(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)
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14 pages, 1935 KiB  
Article
The Development of a Wearable Biofeedback System to Elicit Temporal Gait Asymmetry using Rhythmic Auditory Stimulation and an Assessment of Immediate Effects
by Aliaa Gouda and Jan Andrysek
Sensors 2024, 24(2), 400; https://doi.org/10.3390/s24020400 - 09 Jan 2024
Viewed by 693
Abstract
Temporal gait asymmetry (TGA) is commonly observed in individuals facing mobility challenges. Rhythmic auditory stimulation (RAS) can improve temporal gait parameters by promoting synchronization with external cues. While biofeedback for gait training, providing real-time feedback based on specific gait parameters measured, has been [...] Read more.
Temporal gait asymmetry (TGA) is commonly observed in individuals facing mobility challenges. Rhythmic auditory stimulation (RAS) can improve temporal gait parameters by promoting synchronization with external cues. While biofeedback for gait training, providing real-time feedback based on specific gait parameters measured, has been proven to successfully elicit changes in gait patterns, RAS-based biofeedback as a treatment for TGA has not been explored. In this study, a wearable RAS-based biofeedback gait training system was developed to measure temporal gait symmetry in real time and deliver RAS accordingly. Three different RAS-based biofeedback strategies were compared: open- and closed-loop RAS at constant and variable target levels. The main objective was to assess the ability of the system to induce TGA with able-bodied (AB) participants and evaluate and compare each strategy. With all three strategies, temporal symmetry was significantly altered compared to the baseline, with the closed-loop strategy yielding the most significant changes when comparing at different target levels. Speed and cadence remained largely unchanged during RAS-based biofeedback gait training. Setting the metronome to a target beyond the intended target may potentially bring the individual closer to their symmetry target. These findings hold promise for developing personalized and effective gait training interventions to address TGA in patient populations with mobility limitations using RAS. Full article
(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)
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18 pages, 4061 KiB  
Article
Concurrent Validity of Cervical Movement Tests Using VR Technology—Taking the Lab to the Clinic
by Karin Forsberg, Johan Jirlén, Inger Jacobson and Ulrik Röijezon
Sensors 2023, 23(24), 9864; https://doi.org/10.3390/s23249864 - 16 Dec 2023
Viewed by 770
Abstract
Reduced cervical range of motion (ROM) and movement velocity are often seen in people with neck pain. Objective assessment of movement characteristics is important to identify dysfunction, to inform tailored interventions, and for the evaluation of the treatment effect. The purpose of this [...] Read more.
Reduced cervical range of motion (ROM) and movement velocity are often seen in people with neck pain. Objective assessment of movement characteristics is important to identify dysfunction, to inform tailored interventions, and for the evaluation of the treatment effect. The purpose of this study was to investigate the concurrent validity of a newly developed VR technology for the assessment of cervical ROM and movement velocity. VR technology was compared against a gold-standard three-dimensional optical motion capture system. Consequently, 20 people, 13 without and 7 with neck pain, participated in this quantitative cross-sectional study. ROM was assessed according to right/left rotation, flexion, extension, right/left lateral flexion, and four diagonal directions. Velocity was assessed according to fast cervical rotation to the right and left. The correlations between VR and the optical system for cervical ROM and velocity were excellent, with intraclass correlation coefficient (ICC) values > 0.95. The mean biases between VR and the optical system were ≤ 2.1° for the ROM variables, <12°/s for maximum velocity, and ≤3.0°/s for mean velocity. In conclusion, VR is a useful assessment device for ROM and velocity measurements with clinically acceptable biases. It is a feasible tool for the objective measurement of cervical kinematics in the clinic. Full article
(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)
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