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Advances in Sensing and Robotic Assistive Technologies in Rehabilitation
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Advances in Sensing and Robotic Assistive Technologies in Rehabilitation

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2244

Special Issue Editor

Dr. Daniela D'Auria

E-Mail Website
Guest Editor
Department of Informatics, Systems and Communication; Universiy pf Milan-Bicocca, Piazza dell'Ateneo Nuovo, 1-20126 Milano, Italy
Interests: medical robotics; telemedicine; AI; bioengineering; rehabilitation

Special Issue Information

Dear Colleagues,

In recent years, the intersection of sensing technologies, robotics, and rehabilitation has paved the way for significant improvements in the quality of life of individuals undergoing rehabilitation. The integration of cutting-edge sensor technologies with robotic assistive devices holds immense promise regarding enhancements in the effectiveness and efficiency of rehabilitation programs across various healthcare settings. This Special Issue aims to explore the latest developments and innovations in sensing and robotic assistive technologies in rehabilitation, and will elucidate  the transformative impact of these technologies on patient care and its outcomes.

This Special Issue invites researchers, practitioners, and experts in the fields of sensing technologies, robotics, AI, and rehabilitation to present their original research, reviews, and perspectives on the following topics:

  • Novel sensing technologies for the monitoring and assessment of rehabilitation progress;
  • Robotic assistive devices for physical rehabilitation and mobility enhancement;
  • Human–robot interaction in rehabilitation settings;
  • Wearable sensors and smart devices for at-home rehabilitation;
  • The integration of artificial intelligence and machine learning in sensing and robotic rehabilitation technologies;
  • Wearable sensors for patient telemonitoring and assessment;
  • Clinical applications and case studies showcasing the efficacy of sensing and robotic assistive technologies in rehabilitation.

Dr. Daniela D'Auria
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

  • medical robotics
  • telemedicine
  • AI
  • bioengineering
  • rehabilitation

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Published Papers (1 paper)

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Research

24 pages, 8301 KiB  
Article
Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach
by Subhash Pratap, Jyotindra Narayan, Yoshiyuki Hatta, Kazuaki Ito and Shyamanta M. Hazarika
Sensors 2024, 24(13), 4378; https://doi.org/10.3390/s24134378 - 5 Jul 2024
Cited by 3 | Viewed by 1880
Abstract
Grasp classification is pivotal for understanding human interactions with objects, with wide-ranging applications in robotics, prosthetics, and rehabilitation. This study introduces a novel methodology utilizing a multisensory data glove to capture intricate grasp dynamics, including finger posture bending angles and fingertip forces. Our [...] Read more.
Grasp classification is pivotal for understanding human interactions with objects, with wide-ranging applications in robotics, prosthetics, and rehabilitation. This study introduces a novel methodology utilizing a multisensory data glove to capture intricate grasp dynamics, including finger posture bending angles and fingertip forces. Our dataset comprises data collected from 10 participants engaging in grasp trials with 24 objects using the YCB object set. We evaluate classification performance under three scenarios: utilizing grasp posture alone, utilizing grasp force alone, and combining both modalities. We propose Glove-Net, a hybrid CNN-BiLSTM architecture for classifying grasp patterns within our dataset, aiming to harness the unique advantages offered by both CNNs and BiLSTM networks. This model seamlessly integrates CNNs’ spatial feature extraction capabilities with the temporal sequence learning strengths inherent in BiLSTM networks, effectively addressing the intricate dependencies present within our grasping data. Our study includes findings from an extensive ablation study aimed at optimizing model configurations and hyperparameters. We quantify and compare the classification accuracy across these scenarios: CNN achieved 88.09%, 69.38%, and 93.51% testing accuracies for posture-only, force-only, and combined data, respectively. LSTM exhibited accuracies of 86.02%, 70.52%, and 92.19% for the same scenarios. Notably, the hybrid CNN-BiLSTM proposed model demonstrated superior performance with accuracies of 90.83%, 73.12%, and 98.75% across the respective scenarios. Through rigorous numerical experimentation, our results underscore the significance of multimodal grasp classification and highlight the efficacy of the proposed hybrid Glove-Net architectures in leveraging multisensory data for precise grasp recognition. These insights advance understanding of human–machine interaction and hold promise for diverse real-world applications. Full article
(This article belongs to the Special Issue Advances in Sensing and Robotic Assistive Technologies in Rehabilitation)
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