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Game Sensors in Medicine
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Game Sensors in Medicine

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

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 10831
Please feel free to contact Guest Editors or Special Issue Editor ([email protected]) for any queries.

Special Issue Editors

Prof. Dr. Carsten Eickhoff

E-Mail Website
Guest Editor
Artificial Intelligence Lab, Brown University, Providence, RI, USA
Interests: Information Retrieval, Data Science, Natural Language Processing, Crowdsourcing
Dr. Augusto Garcia-Agundez

E-Mail Website
Guest Editor
AI Lab, Brown Center for Biomedical Informatics, Brown University, Providence, RI 02912, USA
Interests: computer science; sensor technology; serious games; Parkinson´s Disease
Dr. Stefan Göbel

E-Mail Website
Guest Editor
KOM - Multimedia Communications Lab, Technische Universität Darmstadt, 64289 Darmstadt, Germany
Interests: Serious Games; Edutainment; Storytelling; GIS

Special Issue Information

Dear Colleagues,

We are preparing a Special Issue for MDPI Sensors entitled Game Sensors in Medicine. Game sensors hold great promise for domains such as rehabilitation, monitoring, and at-risk patient detection. We will cover aspects related to these sensors and their implementation into clinical practice. MDPI Sensors has a JCR Impact Factor of 3.275 and ranks within the first quartile in the “Instruments & Instrumentation” area.

We are aware of your expertise in this domain and would like to invite you to submit your work. Topics of interest include, but are not limited to:

  • Innovative uses of game sensors (e.g., Wii, Kinect), novel gamified HCI methods (BCI, VR, LiDAR, Ultraleap, Haptics), or other sensors (smartphones, smartwatches, wearables, voice operated interfaces, e-textiles, social networks) with a gaming environment for medical purposes (diagnosis, prognosis, therapy, prevention, monitoring, screening).
  • Advances in signal processing or classification techniques of data acquired thereof.
  • Systematic reviews, meta-analyses, clinical or pilot trials in related applications.

Prof. Dr. Carsten Eickhoff
Dr. Augusto Garcia-Agundez
Dr. Stefan Göbel
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

  • game sensor
  • Wii
  • Kinect
  • BCI
  • VR
  • lidar
  • haptics
  • social networks
  • voice operated interfaces
  • wearables
  • e-textiles
  • rehabilitation
  • telemedicine
  • monitoring

Published Papers (3 papers)

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Research

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20 pages, 2699 KiB  
Article
Recognizing Full-Body Exercise Execution Errors Using the Teslasuit
by Polona Caserman, Clemens Krug and Stefan Göbel
Sensors 2021, 21(24), 8389; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248389 - 15 Dec 2021
Cited by 6 | Viewed by 3934
Abstract
Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, [...] Read more.
Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements. Full article
(This article belongs to the Special Issue Game Sensors in Medicine)
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17 pages, 14408 KiB  
Article
Seeking Inspiration: Examining the Validity and Reliability of a New Smartphone Respiratory Therapy Exergame App
by Clarence Baxter, Julie-Anne Carroll, Brendan Keogh and Corneel Vandelanotte
Sensors 2021, 21(19), 6472; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196472 - 28 Sep 2021
Cited by 4 | Viewed by 2792
Abstract
Background: Clinically valid and reliable simulated inspiratory sounds were required for the development and evaluation of a new therapeutic respiratory exergame application (i.e., QUT Inspire). This smartphone application virtualises incentive spirometry, a longstanding respiratory therapy technique. Objectives: Inspiratory flows were simulated using a [...] Read more.
Background: Clinically valid and reliable simulated inspiratory sounds were required for the development and evaluation of a new therapeutic respiratory exergame application (i.e., QUT Inspire). This smartphone application virtualises incentive spirometry, a longstanding respiratory therapy technique. Objectives: Inspiratory flows were simulated using a 3 litre calibration syringe and validated using clinical reference devices. Syringe flow nozzles of decreasing diameter were applied to model the influence of mouth shape on audible sound levels generated. Methods: A library of calibrated audio inspiratory sounds was created to determine the reliability and range of inspiratory sound detection at increasing distances separating the sound source and smartphones running the app. Results: Simulated inspiratory sounds were reliably detected by the new application at higher air inflows (high, medium), using smaller mouth diameters (<25 mm) and where smartphones were held proximal (≤5 cm) to the mouth (or at distances up to 50 cm for higher airflows). Performance was comparable for popular smartphone types and using different phone orientations (i.e., held horizontally, at 45° or 90°). Conclusions: These observations inform future application refinements, including prompts to reduce mouth diameter, increase inspiratory flow and maintain proximity to the phone to optimise sound detection. This library of calibrated inspiratory sounds offers reproducible non-human reference data suitable for development, evaluation and regression testing of a therapeutic respiratory exergame application for smartphones. Full article
(This article belongs to the Special Issue Game Sensors in Medicine)
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Review

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17 pages, 679 KiB  
Review
Upper Limb Movement Measurement Systems for Cerebral Palsy: A Systematic Literature Review
by Celia Francisco-Martínez, Juan Prado-Olivarez, José A. Padilla-Medina, Javier Díaz-Carmona, Francisco J. Pérez-Pinal, Alejandro I. Barranco-Gutiérrez and Juan J. Martínez-Nolasco
Sensors 2021, 21(23), 7884; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237884 - 26 Nov 2021
Cited by 9 | Viewed by 3092
Abstract
Quantifying the quality of upper limb movements is fundamental to the therapeutic process of patients with cerebral palsy (CP). Several clinical methods are currently available to assess the upper limb range of motion (ROM) in children with CP. This paper focuses on identifying [...] Read more.
Quantifying the quality of upper limb movements is fundamental to the therapeutic process of patients with cerebral palsy (CP). Several clinical methods are currently available to assess the upper limb range of motion (ROM) in children with CP. This paper focuses on identifying and describing available techniques for the quantitative assessment of the upper limb active range of motion (AROM) and kinematics in children with CP. Following the screening and exclusion of articles that did not meet the selection criteria, we analyzed 14 studies involving objective upper extremity assessments of the AROM and kinematics using optoelectronic devices, wearable sensors, and low-cost Kinect sensors in children with CP aged 4–18 years. An increase in the motor function of the upper extremity and an improvement in most of the daily tasks reviewed were reported. In the population of this study, the potential of wearable sensors and the Kinect sensor natural user interface as complementary devices for the quantitative evaluation of the upper extremity was evident. The Kinect sensor is a clinical assessment tool with a unique markerless motion capture system. Few authors had described the kinematic models and algorithms used to estimate their kinematic analysis in detail. However, the kinematic models in these studies varied from 4 to 10 segments. In addition, few authors had followed the joint assessment recommendations proposed by the International Society of Biomechanics (ISB). This review showed that three-dimensional analysis systems were used primarily for monitoring and evaluating spatiotemporal variables and kinematic parameters of upper limb movements. The results indicated that optoelectronic devices were the most commonly used systems. The joint assessment recommendations proposed by the ISB should be used because they are approved standards for human kinematic assessments. This review was registered in the PROSPERO database (CRD42021257211). Full article
(This article belongs to the Special Issue Game Sensors in Medicine)
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