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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 3808

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

29 pages, 5776 KiB  
Article
Intention Reasoning for User Action Sequences via Fusion of Object Task and Object Action Affordances Based on Dempster–Shafer Theory
by Yaxin Liu, Can Wang, Yan Liu, Wenlong Tong and Ming Zhong
Sensors 2025, 25(7), 1992; https://doi.org/10.3390/s25071992 - 22 Mar 2025
Viewed by 312
Abstract
To reduce the burden on individuals with disabilities when operating a Wheelchair Mounted Robotic Arm (WMRA), many researchers have focused on inferring users’ subsequent task intentions based on their “gazing” or “selecting” of scene objects. In this paper, we propose an innovative intention [...] Read more.
To reduce the burden on individuals with disabilities when operating a Wheelchair Mounted Robotic Arm (WMRA), many researchers have focused on inferring users’ subsequent task intentions based on their “gazing” or “selecting” of scene objects. In this paper, we propose an innovative intention reasoning method for users’ action sequences by fusing object task and object action affordances based on Dempster–Shafer Theory (D-S theory). This method combines the advantages of probabilistic reasoning and visual affordance detection to establish an affordance model for objects and potential tasks or actions based on users’ habits and object attributes. This facilitates encoding object task (OT) affordance and object action (OA) affordance using D-S theory to perform action sequence reasoning. Specifically, the method includes three main aspects: (1) inferring task intentions from the object of user focus based on object task affordances encoded with Causal Probabilistic Logic (CP-Logic); (2) inferring action intentions based on object action affordances; and (3) integrating OT and OA affordances through D-S theory. Experimental results demonstrate that the proposed method reduces the number of interactions by an average of 14.085% compared to independent task intention inference and by an average of 52.713% compared to independent action intention inference. This demonstrates that the proposed method can capture the user’s real intention more accurately and significantly reduce unnecessary human–computer interaction. Full article
(This article belongs to the Special Issue Advances in Sensing and Robotic Assistive Technologies in Rehabilitation)
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24 pages, 5261 KiB  
Article
Novel Robotic Balloon-Based Device for Wrist-Extension Therapy of Hemiparesis Stroke Patients
by Klaudia Marek, Aleksandra Olejniczak, Elżbieta Miller and Igor Zubrycki
Sensors 2025, 25(5), 1360; https://doi.org/10.3390/s25051360 - 23 Feb 2025
Viewed by 813
Abstract
Upper-limb paresis is one of the main complications after stroke. It is commonly associated with impaired wrist-extension function. Upper-limb paresis can place a tremendous burden on stroke survivors and their families. A novel soft-actuator device, the Balonikotron, was designed to assist in rehabilitation [...] Read more.
Upper-limb paresis is one of the main complications after stroke. It is commonly associated with impaired wrist-extension function. Upper-limb paresis can place a tremendous burden on stroke survivors and their families. A novel soft-actuator device, the Balonikotron, was designed to assist in rehabilitation by utilizing a balloon mechanism to facilitate wrist-extension exercises. This pilot study aimed to observe the functional changes in the paralyzed upper limb and improvements in independent and cognitive functions following a 4-week regimen using the device, which incorporates a multimedia tablet application providing audiovisual feedback. The device features a cardboard construction with a hinge at wrist level and rails that guide hand movement as the balloon inflates, controlled by a microcontroller and a tablet-based application. It operates on the principle of moving the hand at the wrist by pushing the palm upwards through a surface actuated by a balloon. A model was developed to describe the relationship between the force exerted on the hand, the angle on hinge, the pressure within the balloon, and its volume. Experimental validation demonstrated a Pearson correlation of 0.936 between the model’s force predictions and measured forces, supporting its potential for real-time safety monitoring by automatically shutting down when force thresholds are exceeded. A pilot study was conducted with 12 post-stroke patients (six experimental, six control), who participated in a four-week wrist-extension training program. Clinical outcomes were assessed using the Fugl–Meyer Assessment for the Upper Extremity (FMA-UE), Modified Rankin Scale (mRS), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), wrist Range of Motion (ROM), and Barthel Index (BI). Statistically significant results were obtained for the Barthel index (p < 0.05) and FMA-UE, indicating that the experimental use of the device significantly improved functional independence and self-care abilities. The results of our pilot study suggest that the Balonikotron device, which uses the principles of mirror therapy, may serve as a valuable adjunct to conventional rehabilitation for post-stroke patients with hemiparetic hands (BI p = 0.009, MMSE p = 0.151, mRS p = 0.640, FMA-UE p = 0.045, MOCA p = 0.187, ROM p = 0.109). Full article
(This article belongs to the Special Issue Advances in Sensing and Robotic Assistive Technologies in Rehabilitation)
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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 5 | Viewed by 2323
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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