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Sensor-Based Telerehabilitation and Telemonitoring Technologies
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Sensor-Based Telerehabilitation and Telemonitoring Technologies

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 9067

Special Issue Editor

Dr. Bruno Bonnechère

E-Mail Website
Guest Editor
REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, 3590 Diepenbeek, Belgium
Interests: technology; assessment; big data; rehabilitation; epidemiology; sports science; exercise performance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development, miniaturization, and affordability of new sensors and the associated technologies offer many possibilities in rehabilitation. Amongst the most promising are the solutions developed to facilitate the rehabilitation of patients remotely between traditional sessions, or when supervised sessions are not possible (e.g., interruption of care during the COVID pandemic, lack of infrastructure or health professionals in certain regions of the world).

One of the salient aspects of technology-supported rehabilitation is that a large amount of data can be collected while patients are doing their rehabilitation exercises or during activities of daily living (i.e., walking, sit-to-stand, etc.). These data can be used directly to provide feedback to patients (i.e., increase proprioception), or can be analyzed afterward by clinicians to adapt the rehabilitation according to the patient’s specific abilities and needs. The potential fields of application include not only motor aspects (e.g., walk, balance, coordination) and cognitive functions but also quality of life, autonomy, and independence.

However, before these solutions can be used in the healthcare sector—especially if they are coupled with clinical evaluations—these systems must be fully validated.

Therefore, this Special Issue aims to focus on the development and validation of innovative solutions to support telerehabilitation. This includes both software and hardware validations, but also the development of pipelines (databases) to fully integrate these new solutions in clinics and to better synchronize telerehabilitation, telemonitoring, and conventional care. Finally, in order for data to be used to its maximum potential, machine learning or AI techniques must be developed to determine which variables are, for example, the most important to evaluate in order to predict the evolution of patients or to successfully monitor the risk of a fall.

This Special Issue is intended to be inter- and multidisciplinary in order to evaluate the different aspects of this exciting and rapidly expanding field.

Professor Bruno Bonnechère
Guest Editor

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.

Published Papers (4 papers)

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Research

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26 pages, 10344 KiB  
Article
STASISM: A Versatile Serious Gaming Multi-Sensor Platform for Personalized Telerehabilitation and Telemonitoring
by Anna Kushnir, Oleh Kachmar and Bruno Bonnechère
Sensors 2024, 24(2), 351; https://doi.org/10.3390/s24020351 - 6 Jan 2024
Cited by 1 | Viewed by 1136
Abstract
Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To [...] Read more.
Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient’s progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients’ quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings. Full article
(This article belongs to the Special Issue Sensor-Based Telerehabilitation and Telemonitoring Technologies)
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12 pages, 817 KiB  
Article
Can the Output of a Learned Classification Model Monitor a Person’s Functional Recovery Status Post-Total Knee Arthroplasty?
by Jill Emmerzaal, Arne De Brabandere, Rob van der Straaten, Johan Bellemans, Liesbet De Baets, Jesse Davis, Ilse Jonkers, Annick Timmermans and Benedicte Vanwanseele
Sensors 2022, 22(10), 3698; https://doi.org/10.3390/s22103698 - 12 May 2022
Cited by 4 | Viewed by 2105
Abstract
Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models (probability of belonging to a class) is usable for monitoring a person’s functional recovery status post-total knee [...] Read more.
Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models (probability of belonging to a class) is usable for monitoring a person’s functional recovery status post-total knee arthroplasty (TKA) is largely unexplored. The research question is two-fold: (I) Can a learned classification model’s output be used to monitor a person’s recovery status post-TKA? (II) Is the output related to patient-reported functioning? We constructed a logistic regression model based on (1) pre-operative IMU-data of level walking, ascending, and descending stairs and (2) 6-week post-operative data of walking, ascending-, and descending stairs. Trained models were deployed on subjects at three, six, and 12 months post-TKA. Patient-reported functioning was assessed by the KOOS-ADL section. We found that the model trained on 6-weeks post-TKA walking data showed a decrease in the probability of belonging to the TKA class over time, with moderate to strong correlations between the model’s output and patient-reported functioning. Thus, the LR-model’s output can be used as a screening tool to follow-up a person’s recovery status post-TKA. Person-specific relationships between the probabilities and patient-reported functioning show that the recovery process varies, favouring individual approaches in rehabilitation. Full article
(This article belongs to the Special Issue Sensor-Based Telerehabilitation and Telemonitoring Technologies)
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16 pages, 2748 KiB  
Article
Movement Quality Parameters during Gait Assessed by a Single Accelerometer in Subjects with Osteoarthritis and Following Total Joint Arthroplasty
by Jill Emmerzaal, Kristoff Corten, Rob van der Straaten, Liesbet De Baets, Sam Van Rossom, Annick Timmermans, Ilse Jonkers and Benedicte Vanwanseele
Sensors 2022, 22(8), 2955; https://doi.org/10.3390/s22082955 - 12 Apr 2022
Cited by 10 | Viewed by 2466
Abstract
This study’s aim is threefold: (I) Evaluate movement quality parameters of gait in people with hip or knee osteoarthritis (OA) compared to asymptomatic controls from a single trunk-worn 3D accelerometer. (II) Evaluate the sensitivity of these parameters to capture changes at 6-weeks, 3-, [...] Read more.
This study’s aim is threefold: (I) Evaluate movement quality parameters of gait in people with hip or knee osteoarthritis (OA) compared to asymptomatic controls from a single trunk-worn 3D accelerometer. (II) Evaluate the sensitivity of these parameters to capture changes at 6-weeks, 3-, 6-, and 12-months following total knee arthroplasty (TKA). (III) Investigate whether observed changes in movement quality from 6-weeks and 12-months post-TKA relates to changes in patient-reported outcome measures (PROMs). We invited 20 asymptomatic controls, 20 people with hip OA, 18 people pre- and post-TKA to our movement lap. They wore a single trunk-worn accelerometer and walked at a self-selected speed. Movement quality parameters (symmetry, complexity, smoothness, and dynamic stability) were calculated from the 3D acceleration signal. Between groups and between timepoints comparisons were made, and changes in movement quality were correlated with PROMs. We found significant differences in symmetry and stability in both OA groups. Post-TKA, most parameters reflected an initial decrease in movement quality at 6-weeks post-TKA, which mostly normalised 6-months post-TKA. Finally, improved movement quality relates to improvements in PROMs. Thus, a single accelerometer can characterise movement quality in both OA groups and post-TKA. The correlation shows the potential to monitor movement quality in a clinical setting to inform objective, data-driven personalised rehabilitation. Full article
(This article belongs to the Special Issue Sensor-Based Telerehabilitation and Telemonitoring Technologies)
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14 pages, 909 KiB  
Perspective
Current Technology Developments Can Improve the Quality of Research and Level of Evidence for Rehabilitation Interventions: A Narrative Review
by Bruno Bonnechère, Annick Timmermans and Sarah Michiels
Sensors 2023, 23(2), 875; https://doi.org/10.3390/s23020875 - 12 Jan 2023
Cited by 8 | Viewed by 2247
Abstract
The current important limitations to the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple [...] Read more.
The current important limitations to the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple blinded, strict control of the doses and intensity) cannot—or can only partially—be applied in rehabilitation. Well-powered, high-quality randomized controlled trials are more difficult to organize in rehabilitation (e.g., longer duration of the intervention in rehabilitation, more difficult to standardize the intervention compared to drug validation studies, limited funding since not sponsored by big pharma companies), which reduces the possibility of conducting systematic reviews and meta-analyses, as currently high levels of evidence are sparse. The current limitations of EBP in rehabilitation are presented in this narrative review, and innovative solutions are suggested, such as technology-supported rehabilitation systems, continuous assessment, pragmatic trials, rehabilitation treatment specification systems, and advanced statistical methods, to tackle the current limitations. The development and implementation of new technologies can increase the quality of research and the level of evidence supporting rehabilitation, provided some adaptations are made to our research methodology. Full article
(This article belongs to the Special Issue Sensor-Based Telerehabilitation and Telemonitoring Technologies)
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