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Mobile Robotics/Sensors and Environmental Monitoring Applications for eHealth
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Mobile Robotics/Sensors and Environmental Monitoring Applications for eHealth

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

Deadline for manuscript submissions: closed (25 December 2023) | Viewed by 1259

Special Issue Editor

Prof. Dr. Alberto Brunete

E-Mail Website1 Website2
Guest Editor
Center for Automation and Robotics (CAR UPM-CSIC), Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI), Universidad Politecnica de Madrid, 28012 Madrid, Spain
Interests: smart environments; mobile robotics; cobots; eHealth
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of intelligent robots and sensors in healthcare is growing exponentially in recent years, partly due to COVID-19. Mobile robots are used in hospitals for delivery and handling, for disinfection using ultraviolet light, for mobilizing patients or as assistants to doctors using telepresence.  More generally, they can be used for access control with temperature and identity sensors (for disease detection), for food and medicine delivery using drones and UAVs, or as companions (pets) for disabled people. Not to mention their use in prostheses, exoskeletons for rehabilitation, detection of gait pathologies or eye movements.

This special issue will bring together advances in health applications based on mobile robots, robotic arms, smart sensors and IoT devices that improve the way of life of people or medical staff.

Submissions are welcome but not limited to the following topics related to eHealth:

  • Mobile robots for telepresence or delivery
  • Cobots for medical handling
  • Biomechanical analysis of human gait
  • Fall detection and prevention
  • Smart rooms for disabled people
  • Exoskeletons for rehabilitation
  • Eye movement detection
  • Sensor fusion
  • HMI
  • Machine learning

Prof. Dr. Alberto Brunete
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.

Keywords

  • mobile robots
  • cobots
  • IoT
  • HMI
  • machine learning
  • biomechanics
  • exoskeletons
  • rehabilitation
  • falls

Published Papers (1 paper)

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Research

11 pages, 3770 KiB  
Article
Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios
by Diego Guffanti, Daniel Lemus, Heike Vallery, Alberto Brunete, Miguel Hernando and Herwin Horemans
Sensors 2023, 23(15), 6944; https://doi.org/10.3390/s23156944 - 4 Aug 2023
Cited by 1 | Viewed by 1018
Abstract
Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, [...] Read more.
Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, assessment of gait consistency requires testing over a longer walking distance. The aim of this study is to validate the accuracy for gait assessment of a previously developed method that determines walking spatiotemporal parameters and kinematics measured with a 3D camera mounted on a mobile robot base (ROBOGait). Walking parameters measured with this system were compared with measurements with Xsens IMUs. The experiments were performed on a non-linear corridor of approximately 50 m, resembling the environment of a conventional rehabilitation facility. Eleven individuals exhibiting normal motor function were recruited to walk and to simulate gait patterns representative of common neurological conditions: Cerebral Palsy, Multiple Sclerosis, and Cerebellar Ataxia. Generalized estimating equations were used to determine statistical differences between the measurement systems and between walking conditions. When comparing walking parameters between paired measures of the systems, significant differences were found for eight out of 18 descriptors: range of motion (ROM) of trunk and pelvis tilt, maximum knee flexion in loading response, knee position at toe-off, stride length, step time, cadence; and stance duration. When analyzing how ROBOGait can distinguish simulated pathological gait from physiological gait, a mean accuracy of 70.4%, a sensitivity of 49.3%, and a specificity of 74.4% were found when compared with the Xsens system. The most important gait abnormalities related to the clinical conditions were successfully detected by ROBOGait. The descriptors that best distinguished simulated pathological walking from normal walking in both systems were step width and stride length. This study underscores the promising potential of 3D cameras and encourages exploring their use in clinical gait analysis. Full article
(This article belongs to the Special Issue Mobile Robotics/Sensors and Environmental Monitoring Applications for eHealth)
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