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Advanced Sensing Technologies for Tele-Assessment and Tele-Rehabilitation
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Advanced Sensing Technologies for Tele-Assessment and Tele-Rehabilitation

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 8491

Special Issue Editors

Dr. Milad Nazarahari

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Alberta, Edmonton, AB, Canada
Interests: biomedical engineering; wearable and robotic rehabilitation technologies; motor control; biomedical signal processing and artificial intelligence
Prof. Dr. Christian Peham

E-Mail Website
Guest Editor
Department for Companion Animals and Horses, University of Veterinary Medicine, Veterinärplatz 1, 1210 Vienna, Austria
Interests: equine biomechanics; motion analysis; canine biomechanics; muskolo-skeletal modelling and simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in sensing technologies, especially wearable technologies, have provided an unprecedented opportunity for tele-assessment and tele-rehabilitation. Sensing technologies such as motion trackers and physiological sensors, with light weight and long battery life, can measure (remotely) human movement and physiological signals, thus allowing us to predict a disease, objectively quantify the consequences of a disease, and finally deliver therapy. This is particularly important when we consider accessibility (in almost every household) and affordability of these sensing technologies (e.g., smartwatches and smartphones). Additionally, combined with artificial intelligence, the large volume of data measured with these sensing technologies can answer questions, which was not possible before. Nevertheless, the application of sensing technologies is not limited to clinical settings, and they have been shown to be promising in many other settings such as sport engineering and ergonomics.

Thus, this Special Issue aims to put together original research and review articles presenting recent advances, novel technologies and algorithms, technical and clinical applications, and finally challenges pertaining to sensing technologies for tele-assessment and tele-rehabilitation.

Potential topics include, but are not limited to:

  • Human activity monitoring;
  • Human physiological signal measurement;
  • Digital health;
  • Precision medicine;
  • (Tele)Assessment;
  • (Tele)Rehabilitation;
  • Sprot injury risk prevention;
  • Work-related injury prevention;
  • Human factors;
  • Wearable sensor technologies;
  • Smartphone/smartwatch;
  • Inertial measurement units;
  • Electromyography (EMG);
  • Galvanic skin response (GSR);
  • Pressure insoles;
  • EEG;
  • Artificial intelligence;
  • Machine learning;
  • Sensor fusion.

Dr. Milad Nazarahari
Prof. Dr. Christian Peham
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.

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Published Papers (3 papers)

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Research

17 pages, 1969 KiB  
Article
A Machine Learning App for Monitoring Physical Therapy at Home
by Bruno Pereira, Bruno Cunha, Paula Viana, Maria Lopes, Ana S. C. Melo and Andreia S. P. Sousa
Sensors 2024, 24(1), 158; https://doi.org/10.3390/s24010158 - 27 Dec 2023
Cited by 4 | Viewed by 2743
Abstract
Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This [...] Read more.
Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Tele-Assessment and Tele-Rehabilitation)
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16 pages, 721 KiB  
Article
A Deep Learning Approach for Automatic and Objective Grading of the Motor Impairment Severity in Parkinson’s Disease for Use in Tele-Assessments
by Mehar Singh, Prithvi Prakash, Rachneet Kaur, Richard Sowers, James Robert Brašić and Manuel Enrique Hernandez
Sensors 2023, 23(21), 9004; https://doi.org/10.3390/s23219004 - 6 Nov 2023
Cited by 2 | Viewed by 2660
Abstract
Wearable sensors provide a tool for at-home monitoring of motor impairment progression in neurological conditions such as Parkinson’s disease (PD). This study examined the ability of deep learning approaches to grade the motor impairment severity in a modified version of the Movement Disorders [...] Read more.
Wearable sensors provide a tool for at-home monitoring of motor impairment progression in neurological conditions such as Parkinson’s disease (PD). This study examined the ability of deep learning approaches to grade the motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. We hypothesized that expanding training datasets with motion data from healthy older adults (HOAs) and initializing classifiers with weights learned from unsupervised pre-training would lead to an improvement in performance when classifying lower vs. higher motor impairment relative to a baseline deep learning model (XceptionTime). This study evaluated the change in classification performance after using expanded training datasets with HOAs and transferring weights from unsupervised pre-training compared to a baseline deep learning model (XceptionTime) using both upper extremity (finger tapping, hand movements, and pronation–supination movements of the hands) and lower extremity (toe tapping and leg agility) tasks consistent with the MDS-UPDRS. Overall, we found a 12.2% improvement in accuracy after expanding the training dataset and pre-training using max-vote inference on hand movement tasks. Moreover, we found that the classification performance improves for every task except toe tapping after the addition of HOA training data. These findings suggest that learning from HOA motion data can implicitly improve the representations of PD motion data for the purposes of motor impairment classification. Further, our results suggest that unsupervised pre-training can improve the performance of motor impairment classifiers without any additional annotated PD data, which may provide a viable solution for a widely deployable telemedicine solution. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Tele-Assessment and Tele-Rehabilitation)
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9 pages, 2293 KiB  
Communication
A Dynamic Procedure to Detect Maximum Voluntary Contractions in Low Back
by Xun Wang, Karla Beltran Martinez, Ali Golabchi, Mahdi Tavakoli and Hossein Rouhani
Sensors 2023, 23(11), 4999; https://doi.org/10.3390/s23114999 - 23 May 2023
Cited by 4 | Viewed by 2419
Abstract
Surface electromyography (sEMG) is generally used to measure muscles’ activity. The sEMG signal can be affected using several factors and vary among individuals and even measurement trials. Thus, to consistently evaluate data among individuals and trials, the maximum voluntary contraction (MVC) value is [...] Read more.
Surface electromyography (sEMG) is generally used to measure muscles’ activity. The sEMG signal can be affected using several factors and vary among individuals and even measurement trials. Thus, to consistently evaluate data among individuals and trials, the maximum voluntary contraction (MVC) value is usually calculated and used to normalize sEMG signals. However, the sEMG amplitude collected from low back muscles can be frequently larger than that found when conventional MVC measurement procedures are used. To address this limitation, in this study, we proposed a new dynamic MVC measurement procedure for low back muscles. Inspired by weightlifting, we designed a detailed dynamic MVC procedure, and then collected data from 10 able-bodied participants and compared their performances using several conventional MVC procedures by normalizing the sEMG amplitude for the same test. The sEMG amplitude normalized by our dynamic MVC procedure showed a much lower value than those obtained using other procedures (Wilcoxon signed-rank test, with p < 0.05), indicating that the sEMG collected during dynamic MVC procedure had a larger amplitude than those of conventional MVC procedures. Therefore, our proposed dynamic MVC obtained sEMG amplitudes closer to its physiological maximum value and is thus more capable of normalizing the sEMG amplitude for low back muscles. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Tele-Assessment and Tele-Rehabilitation)
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