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Wearable and Integrated Sensors for Sport and Rehabilitation Applications II

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

Deadline for manuscript submissions: closed (25 May 2023) | Viewed by 14190

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Special Issue Editors

Dr. Anton Kos

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Guest Editor

Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
Interests: biofeedback systems; kinematic sensors; sensor systems in sport; smart sport equipment; signal processing; communication networks; information systems; dataflow computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sašo Tomažič

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Guest Editor

Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
Interests: information and communication technologies; signal processing; information theory; data mining and knowledge discovery; sensors; feedback systems: biomechanical; electrical; monetary; social; economic
Special Issues, Collections and Topics in MDPI journals

Dr. Anton Umek

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Guest Editor

Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
Interests: signal processing; information and communication theory; communication systems and technologies; biofeedback systems and applications; wearable sensors and smart sport equipment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent developments in miniature sensors have led to their integration into a range of everyday devices, such as smartphones and smart watches; into simple wearable devices such as wristbands; and into professional wearable sensor devices and smart equipment used in sports and physical rehabilitation.

Sensors integrated into wearable devices and smart equipment offer new insights into the actions and movements of the human body and represent an important advancement in measuring and quantifying them. For example, they measure or detect kinematic parameters that were previously not available or provide precise measurements of parameters that are not observable with the naked eye.

In order to achieve the maximal benefit, the acquired sensor signals and data should be processed and analyzed appropriately and in a timely manner. A wide range of options is available, from the on-board real-time calculation of basic parameters inside the wearable sensor device or smart equipment to deep-learning methods implemented on powerful computing platforms. The processing and analysis results can range from simple event counting and statistics to complex intelligent systems that give advice to professionals (coaches and therapists) or provide real-time feedback to users (athletes and patients).

Owing to the success of the first volume, we have worked to create this second one. This Special Issue intends to gather high-quality, state-of-the-art research papers and up-to-date reviews that address challenging topics related to the use of wearable and integrated sensors in sports and physical rehabilitation. We request original papers featuring unpublished and completed research that are not currently under review by any other journal. Topics of interest include but are not limited to the following:

Sensors and sensor devices in sport and rehabilitation;
Wearable devices and smart equipment in sport and rehabilitation;
Sensor systems and applications in sport and rehabilitation;
Feedback systems and applications in sport and rehabilitation;
Advanced sensor signal processing and data analysis methods in sport and rehabilitation;
Communication technologies for sensor systems and smart equipment;
Movement and activity recognition;
Systems, applications, and methods for providing biofeedback in sport and rehabilitation.

If you have any suggestions that you would like to discuss in advance, please do not hesitate to contact us. We look forward to your participation and welcome you to submit a paper to this Special Issue.

Dr. Anton Kos
Prof. Dr. Sašo Tomažič
Dr. Anton Umek
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 and integrated sensors
sport and rehabilitation
sensor devices and systems
smart equipment
sensor communication technologies
sensor signal processing
data analysis
sensor-signal-based machine learning
biofeedback systems and applications
intelligent sensor systems
movement and activity recognition

Published Papers (4 papers)

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Research

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17 pages, 2904 KiB

Open AccessArticle

Reliability and Discriminative Validity of Wearable Sensors for the Quantification of Upper Limb Movement Disorders in Individuals with Dyskinetic Cerebral Palsy

by Inti Vanmechelen, Saranda Bekteshi, Helga Haberfehlner, Hilde Feys, Kaat Desloovere, Jean-Marie Aerts and Elegast Monbaliu

Sensors 2023, 23(3), 1574; https://doi.org/10.3390/s23031574 - 1 Feb 2023

Viewed by 1336

Abstract

Background—Movement patterns in dyskinetic cerebral palsy (DCP) are characterized by abnormal postures and involuntary movements. Current evaluation tools in DCP are subjective and time-consuming. Sensors could yield objective information on pathological patterns in DCP, but their reliability has not yet been evaluated. The objectives of this study were to evaluate (i) reliability and (ii) discriminative ability of sensor parameters. Methods—Inertial measurement units were placed on the arm, forearm, and hand of individuals with and without DCP while performing reach-forward, reach-and-grasp-vertical, and reach-sideways tasks. Intra-class correlation coefficients (ICC) were calculated for reliability, and Mann–Whitney U-tests for between-group differences. Results—Twenty-two extremities of individuals with DCP (mean age 16.7 y) and twenty individuals without DCP (mean age 17.2 y) were evaluated. ICC values for all sensor parameters except jerk and sample entropy ranged from 0.50 to 0.98 during reach forwards/sideways and from 0.40 to 0.95 during reach-and-grasp vertical. Jerk and maximal acceleration/angular velocity were significantly higher for the DCP group in comparison with peers. Conclusions—This study was the first to assess the reliability of sensor parameters in individuals with DCP, reporting high between- and within-session reliability for the majority of the sensor parameters. These findings suggest that pathological movements of individuals with DCP can be reliably captured using a selection of sensor parameters. Full article

(This article belongs to the Special Issue Wearable and Integrated Sensors for Sport and Rehabilitation Applications II)

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8 pages, 1753 KiB

Open AccessArticle

A Method for Accessing the Non-Slip Function of Socks in an Acute Maneuver

by Dongwook Seo, Jinsu Eun, Yeonwoo Yu, Sangsoo Park and Kikwang Lee

Sensors 2023, 23(3), 1378; https://doi.org/10.3390/s23031378 - 26 Jan 2023

Viewed by 1640

Abstract

The shoe upper hides the foot motion on the insole, so it has been challenging to measure the non-slip function of socks in a dynamic motor task. The study aimed to propose a method to estimate the non-slip function of socks in an acute maneuver. Participants performed a shuttle run task while wearing three types of socks with different frictional properties. The forces produced by foot movement on the upper during the task were measured by pressure sensors installed at the upper. A force platform was also used to measure the ground reaction force at the outsole and ground. Peak force and impulse values computed by using forces measured by the pressure sensors were significantly different between the sock conditions, while there were no such differences in those values computed by using ground reaction forces measured by a force platform. The results suggested that the non-slip function of socks could be quantified by measuring forces at the foot-upper interface. The method could be an affordable option to measure the non-slip function of socks with minimal effects from skin artifacts and shoe upper integrity. Full article

(This article belongs to the Special Issue Wearable and Integrated Sensors for Sport and Rehabilitation Applications II)

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10 pages, 1102 KiB

Open AccessArticle

Inner-Cycle Phases Can Be Estimated from a Single Inertial Sensor by Long Short-Term Memory Neural Network in Roller-Ski Skating

by Frédéric Meyer, Magne Lund-Hansen, Trine M. Seeberg, Jan Kocbach, Øyvind Sandbakk and Andreas Austeng

Sensors 2022, 22(23), 9267; https://doi.org/10.3390/s22239267 - 28 Nov 2022

Viewed by 1153

Abstract

Objective: The aim of this study was to provide a new machine learning method to determine temporal events and inner-cycle parameters (e.g., cycle, pole and ski contact and swing time) in cross-country roller-ski skating on the field, using a single inertial measurement unit (IMU). Methods: The developed method is based on long short-term memory neural networks to detect the initial and final contact of the poles and skis with the ground during the cyclic movements. Eleven athletes skied four laps of 2.5 km at a low and high intensity using skis with two different rolling coefficients. They were equipped with IMUs attached to the upper back, lower back and to the sternum. Data from force insoles and force poles were used as the reference system. Results: The IMU placed on the upper back provided the best results, as the LSTM network was able to determine the temporal events with a mean error ranging from −1 to 11 ms and had a standard deviation (SD) of the error between 64 and 70 ms. The corresponding inner-cycle parameters were calculated with a mean error ranging from −11 to 12 ms and an SD between 66 and 74 ms. The method detected 95% of the events for the poles and 87% of the events for the skis. Conclusion: The proposed LSTM method provides a promising tool for assessing temporal events and inner-cycle phases in roller-ski skating, showing the potential of using a single IMU to estimate different spatiotemporal parameters of human locomotion. Full article

(This article belongs to the Special Issue Wearable and Integrated Sensors for Sport and Rehabilitation Applications II)

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Review

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19 pages, 985 KiB

Open AccessReview

Immersive Virtual Reality in Post-Stroke Rehabilitation: A Systematic Review

by Andrea Demeco, Laura Zola, Antonio Frizziero, Chiara Martini, Arrigo Palumbo, Ruben Foresti, Giovanni Buccino and Cosimo Costantino

Sensors 2023, 23(3), 1712; https://doi.org/10.3390/s23031712 - 3 Feb 2023

Cited by 26 | Viewed by 9421

Abstract

In recent years, next to conventional rehabilitation’s techniques, new technologies have been applied in stroke rehabilitation. In this context, fully immersive virtual reality (FIVR) has showed interesting results thanks to the level of immersion of the subject in the illusional world, with the feeling of being a real part of the virtual environment. This study aims to investigate the efficacy of FIVR in stroke rehabilitation. PubMed, Web of Science and Scopus were screened up to November 2022 to identify eligible randomized controlled trials (RCTs). Out of 4623, we included 12 RCTs involving post-acute and chronic stroke survivors, with a total of 350 patients (234 men and 115 women; mean age 58.36 years). High heterogeneity of the outcomes considered, the results showed that FIVR provides additional benefits, in comparison with standard rehabilitation. In particular, results showed an improvement in upper limb dexterity, gait performance and dynamic balance, influencing patient independence. Therefore, FIVR represents an adaptable, multi-faceted rehabilitation tool that can be considered in post-stroke rehabilitation, improving the compliance of the patients to the treatment and increasing the level of functioning and quality of life of stroke survivors. Full article

(This article belongs to the Special Issue Wearable and Integrated Sensors for Sport and Rehabilitation Applications II)

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