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Advanced Artificial Devices and Sensing Technologies in Rehabilitation

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

Deadline for manuscript submissions: 30 August 2025 | Viewed by 503

Special Issue Editors


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Guest Editor
Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Elsene, Belgium
Interests: machine learning; serious gaming; deep learning; biomedical signal and image analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
Interests: rehabilitation; sport science; biomechanics; sport injuries; musculoskeletal disorders
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue is looking for papers that contribute to the use of AI and intelligent sensors in the domain of physical and/or cognitive rehabilitation. This rapidly advancing field offers a wide array of technological innovations aimed at enhancing the rehabilitation process across various contexts. Potential contributions may focus on diverse applications such as AI-driven computer games designed to promote motor or cognitive recovery, virtual and digital twins for personalized rehabilitation programs, and predictive models for patients’ outcomes or treatment efficacy. Additionally, the use of data from wearable devices to track patient progress in real time during the rehabilitation process (e.g., rehabilomics), camera-based systems for motion analysis, and other sensor-based technologies that provide novel approaches for monitoring and assessing rehabilitation efforts are welcome.

Dr. Bart Jansen
Dr. Bruno Bonnechère
Guest Editors

Manuscript Submission Information

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Keywords

  • AR/VR
  • digital twin
  • prediction models
  • wearables
  • mocap
  • computer games
  • rehabilomics
  • serious games

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Published Papers (1 paper)

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Research

29 pages, 3296 KiB  
Article
Investigating the Neural Mechanisms of Self-Controlled and Externally Controlled Movement with a Flexible Exoskeleton Using EEG Source Localization
by Takayuki Kodama, Masahiro Yoshikawa, Kosuke Minamii, Kazuhei Nishimoto, Sayuna Kadowaki, Yuuki Inoue, Hiroki Ito, Hayato Shigeto, Kohei Okuyama, Kouta Maeda, Osamu Katayama, Shin Murata and Kiichiro Morita
Sensors 2025, 25(11), 3527; https://doi.org/10.3390/s25113527 - 3 Jun 2025
Viewed by 173
Abstract
Background: Self-controlled motor imagery combined with assistive devices is promising for enhancing neurorehabilitation. This study developed a soft, Flexible Exoskeleton (flexEXO) for finger movements and investigated whether self-controlled motor tasks facilitate stronger cortical activation than externally controlled conditions. Methods: Twenty-one healthy participants performed [...] Read more.
Background: Self-controlled motor imagery combined with assistive devices is promising for enhancing neurorehabilitation. This study developed a soft, Flexible Exoskeleton (flexEXO) for finger movements and investigated whether self-controlled motor tasks facilitate stronger cortical activation than externally controlled conditions. Methods: Twenty-one healthy participants performed grasping tasks under four conditions: Self-Controlled Motion (SCC), Other-Controlled Motion (OCC), Self-Controlled Imagery Only (SCIOC), and Other-Controlled Imagery Only (OCIOC). EEG data were recorded, focusing on event-related desynchronization (ERD) in the μ and β bands during imagery and motion and event-related synchronization (ERS) in the β band during feedback. Source localization was performed using eLORETA. Results: Higher μERD and βERD were observed during self-controlled tasks, particularly in the primary motor cortex and supplementary motor area. Externally controlled tasks showed enhanced activation in the inferior parietal lobule and secondary somatosensory cortex. βERS did not differ significantly across conditions. Source localization revealed that self-controlled tasks engaged motor planning and error-monitoring regions more robustly. Conclusions: The flexEXO device and the comparison of brain activity under different conditions provide insights into the neural mechanisms of motor control and have implications for neurorehabilitation. Full article
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