Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Sensor-Based Human Motor Learning
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensor-Based Human Motor Learning

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 4386

Special Issue Editors

Dr. Xavier García-Massó

E-Mail Website
Guest Editor
Human Movement Analysis Group, University of Valencia, Valencia, Spain
Interests: motor learning in children and adolescence
Special Issues, Collections and Topics in MDPI journals
Dr. Israel Villarrasa-Sapiña

E-Mail Website
Guest Editor
Human Movement Analysis Group, University of Valencia, Valencia, Spain
Interests: motor control; motor learning; physical activity; exercise; sport; obesity; postural control
Dr. Cristina Menescardi

E-Mail Website
Guest Editor
AFIPS, University of Valencia, Valencia, Spain
Interests: motor competente; physical literacy; observational methodology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decades the human society has experimented a huge technological development. As a result, several sensors are available to monitor and assess motor functioning both in healthy and people with health troubles. This sprawl of sensors to evaluate movement provide the opportunity to use it not only as a way to determine the current motor state of people but also to be used as a training involved tool to improve motor function. Therefore, the main aim of this special issue is to publish scientific evidence on the use of different sensors to promote motor learning.

Potential topics include but are not limited to:

  • Clinical use of sensors in motor function recovery.
  • Sensors in educational contexts to facilitate motor learning.
  • Motion sensors applied in sports as training tool.
  • Development of wearable sensors to facilitate motor learning.
  • New strategies to evaluate motor learning through sensing technology.

Dr. Xavier García-Massó
Dr. Israel Villarrasa-Sapiña
Dr. Cristina Menescardi
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

  • motor learning
  • inertial measurement unit
  • motor function
  • physical training
  • motor sensors
  • physical conditioning
  • feedback

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

11 pages, 1932 KiB  
Article
Effect of Reduced Feedback Frequencies on Motor Learning in a Postural Control Task in Young Adults
by Adrià Marco-Ahulló, Israel Villarrasa-Sapiña, Jorge Romero-Martínez, Gonzalo Monfort-Torres, Jose Luis Toca-Herrera and Xavier García-Massó
Sensors 2024, 24(5), 1404; https://0-doi-org.brum.beds.ac.uk/10.3390/s24051404 - 22 Feb 2024
Viewed by 902
Abstract
The effects of the use of reduced feedback frequencies on motor learning remain controversial in the scientific literature. At present, there is still controversy about the guidance hypothesis, with some works supporting it and others contradicting it. To shed light on this topic, [...] Read more.
The effects of the use of reduced feedback frequencies on motor learning remain controversial in the scientific literature. At present, there is still controversy about the guidance hypothesis, with some works supporting it and others contradicting it. To shed light on this topic, an experiment was conducted with four groups, each with different feedback frequencies (0%, 33%, 67%, and 100%), which were evaluated three times (pre-test, post-test, and retention) during a postural control task. In addition, we tested whether there was a transfer in performance to another similar task involving postural control. As a result, only the 67% feedback group showed an improvement in their task performance in the post-test and retention evaluations. Nevertheless, neither group showed differences in motor transfer performance compared to another postural control task. In conclusion, the findings of this paper corroborate the hypothesis of guidance and suggest that the use of a reduced frequency of 67% is a better option for improving motor learning than options that offer feedback at a lower frequency, at all trials or not at all. Full article
(This article belongs to the Special Issue Sensor-Based Human Motor Learning)
►▼ Show Figures

Figure 1

18 pages, 2875 KiB  
Article
Initial Testing of Robotic Exoskeleton Hand Device for Stroke Rehabilitation
by Rami Alhamad, Nitin Seth and Hussein A. Abdullah
Sensors 2023, 23(14), 6339; https://0-doi-org.brum.beds.ac.uk/10.3390/s23146339 - 12 Jul 2023
Cited by 1 | Viewed by 1651
Abstract
The preliminary test results of a novel robotic hand rehabilitation device aimed at treatment for the loss of motor abilities in the fingers and thumb due to stroke are presented. This device has been developed in collaboration with physiotherapists who regularly treat individuals [...] Read more.
The preliminary test results of a novel robotic hand rehabilitation device aimed at treatment for the loss of motor abilities in the fingers and thumb due to stroke are presented. This device has been developed in collaboration with physiotherapists who regularly treat individuals who have suffered from a stroke. The device was tested on healthy adults to ensure comfort, user accessibility, and repeatability for various hand sizes in preparation for obtaining permission from regulatory bodies and implementing the design in a full clinical trial. Trials were conducted with 52 healthy individuals ranging in age from 19 to 93 with an average age of 58. A comfort survey and force data ANOVA were performed to measure hand motions and ensure the repeatability and accessibility of the system. Readings from the force sensor (p < 0.05) showed no significant difference between repetitions for each participant. All subjects considered the device comfortable. The device scored a mean comfort value of 8.5/10 on all comfort surveys and received the approval of all physiotherapists involved. The device has satisfied all design specifications, and the positive results of the participants suggest that it can be considered safe and reliable. It can therefore be moved forward for clinical trials with post-stroke users. Full article
(This article belongs to the Special Issue Sensor-Based Human Motor Learning)
►▼ Show Figures

Figure 1

10 pages, 1093 KiB  
Article
Human Motor Noise Assessed by Electromagnetic Sensors and Its Relationship with the Degrees of Freedom Involved in Movement Control
by Carla Caballero, David Barbado and Francisco J. Moreno
Sensors 2023, 23(4), 2256; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042256 - 17 Feb 2023
Cited by 1 | Viewed by 1213
Abstract
Motor variability is a prominent feature of the human movement that, nowadays, can be easily measured through different sensors and analyzed using different types of variables, and it seems to be related to functional and adaptative motor behavior. It has been stated that [...] Read more.
Motor variability is a prominent feature of the human movement that, nowadays, can be easily measured through different sensors and analyzed using different types of variables, and it seems to be related to functional and adaptative motor behavior. It has been stated that motor variability is related to the system’s flexibility needed to choose the right degrees of freedom (DoFs) to adapt to constant environmental changes. However, the potential relationship between motor variability and DoFs is unknown. The aim of this study was to analyze how motor variability, both the amount and structure, changes depending on the mechanical DoFs involved in the movement control. For this purpose, movement variability was assessed by a tracking sensor in five tasks with different DoFs, and the amount, using standard deviation, and the structure of variability, through fuzzy entropy and detrended fluctuation analysis, were also assessed. The results showed a higher amount of variability and a less predictable and more auto-correlated variability structure in the long-term when more mechanical DoFs are implied. The studies that analyze motor variability should consider the type of movement and the DoFs involved in the analyzed task since, as the findings have shown, both factors have a noticeable influence on the amount and the structure of motor variability. Full article
(This article belongs to the Special Issue Sensor-Based Human Motor Learning)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop