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Assistive Robots for Healthcare and Human-Robot Interaction: 3rd Edition

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

Deadline for manuscript submissions: closed (15 November 2025) | Viewed by 1898

Special Issue Editors


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Guest Editor
Complex Unit of Geriatrics, Department of Medical Sciences, Fondazione Casa Sollievo della Sofferenza, Viale Cappuccini, 1, 71013 San Giovanni Rotondo, Italy
Interests: geriatrics; neurocognitive disorders; psychological and behavioural symptoms; information and communication technologies; ambient assisted living
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Complex Unit of Geriatrics, Department of Medical Sciences, Fondazione Casa Sollievo della Sofferenza, Viale Cappuccini, 1, 71013 San Giovanni Rotondo, Italy
Interests: genetics; pharmacology; cognition disorders; neurodegenerative diseases; memory; clinical neuropsychology; cognitive neuropsychology; executive function; cognitive neuroscience; learning and memory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Assistive technologies such as Assistive Robots (ARs) could support the process of care giving, potentially enhancing patient well-being and decreasing the workload of caregivers. However, research concerning person-centered care, multimodal interaction, multimodal data collection, and caregiver expectancy models must be performed in order to improve the acceptability of ARs.

As such, the field of Human–Robot Interaction (HRI) is devoted to understanding, designing, and assessing the robotic systems utilized by human beings.

By definition, this interaction requires communication, thus, research in the field of HRI is increasingly focused on the development of robots equipped with intelligent communicative abilities, particularly speech-based natural-language conversational abilities. These efforts directly relate to the research area of computational linguistics, which is generally defined as “the subfield of computer science concerned with using computational techniques to learn, understand, and produce human language content”. The advances and results obtained in computational linguistics provide a foundation for the development of so-called Spoken Dialogue Systems, i.e., computer systems designed to interact with humans using spoken natural language. The ability to communicate using natural language is a fundamental requirement for a robot that interacts with human beings, as spoken dialogue is generally considered the most natural means of social human–robot interaction. Because sensing technologies play a key role in HRI, novel approaches or the application of existing techniques in a novel way may lead to advances in this and related fields.

This Special Issues aims to advance the application of novel technologies in healthcare processes that have shown exceptional promise in HRI via the use of new sensors or methodologies that are able to adapt, combine or enhance the existing ones. Therefore, this Special Issue welcomes the submission of articles that address concerns regarding the modalities needed to sense the emotional state of people by robots, and those related to modelling the interaction between humans and robots at both the haptic and emotional level.

Dr. Grazia D'Onofrio
Dr. Daniele Sancarlo
Guest Editors

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Keywords

  • development of new sensing methodologies to facilitate HRI
  • improvement of existing technologies in HRI
  • application of multimodal approaches in HRI
  • role of emotional detection in HRI
  • ethical aspects of HRI
  • sensitive design in care robotics
  • patient centeredness
  • acceptability and usability assessment
  • impact of robot embodiment and how this affects interactions

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Published Papers (1 paper)

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Research

34 pages, 6603 KB  
Article
Intelligent Dental Handpiece: Real-Time Motion Analysis for Skill Development
by Mohamed Sallam, Yousef Salah, Yousef Osman, Ali Hegazy, Esraa Khatab and Omar Shalash
Sensors 2025, 25(20), 6489; https://doi.org/10.3390/s25206489 - 21 Oct 2025
Cited by 2 | Viewed by 821
Abstract
Modern dental education increasingly calls for smarter tools that combine precision with meaningful feedback. In response, this study presents the Intelligent Dental Handpiece (IDH), a next-generation training tool designed to support dental students and professionals by providing real-time insights into their techniques. The [...] Read more.
Modern dental education increasingly calls for smarter tools that combine precision with meaningful feedback. In response, this study presents the Intelligent Dental Handpiece (IDH), a next-generation training tool designed to support dental students and professionals by providing real-time insights into their techniques. The IDH integrates motion sensors and a lightweight machine learning system to monitor and classify hand movements during practice sessions. The system classifies three motion states: Alert (10°–15° deviation), Lever Range (0°–10°), and Stop Range (>15°), based on IMU-derived features. A dataset collected from 61 practitioners was used to train and evaluate three machine learning models: Logistic Regression, Random Forest, Support Vector Machine (Linear RBF, Polynomial kernels), and a Neural Network. Performance across models ranged from 98.52% to 100% accuracy, with Random Forest and Logistic Regression achieving perfect classification and AUC scores of 1.00. Motion features such as Deviation, Take Time, and Device type were most influential in predicting skill levels. The IDH offers a practical and scalable solution for improving dexterity, safety, and confidence in dental training environments. Full article
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