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Applied Sciences
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New Insights into Rehabilitation Robots with Intelligent Sensing Systems
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New Insights into Rehabilitation Robots with Intelligent Sensing Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 5578

Special Issue Editors

Dr. Kai Guo

E-Mail Website
Guest Editor
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Interests: rehabilitation medicine; rehabilitation engineering; hand rehabilitation robots
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hongbo Yang

E-Mail Website
Guest Editor
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Interests: rehabilitation engineering; medical flexible sensing; simulation design of optical; mechanical and electrical integration; mechanical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the aging population and the increase in chronic diseases, rehabilitation robots are becoming increasingly important in rehabilitation medicine. The introduction of intelligent sensing systems enables rehabilitation robots to better understand and adapt to the needs of patients, improving the outcomes and quality of life for patients. This Special Issue aims to explore the latest applications of and developments in intelligent sensing technology in rehabilitation robots, providing researchers and clinicians with new perspectives and an in-depth understanding of this technology.

This Special Issue covers the design, optimization, and application of intelligent sensing systems in rehabilitation robots, as well as their specific applications in neurorehabilitation, musculoskeletal rehabilitation, and sports rehabilitation. These studies will provide important guidance for the future development of rehabilitation robots, helping to improve their functionality and applicability, and further advance the field of rehabilitation medicine.

Dr. Kai Guo
Prof. Dr. Hongbo Yang
Guest Editors

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. Applied Sciences 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 2400 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

  • neurological rehabilitation
  • musculoskeletal rehabilitation
  • sports rehabilitation
  • intelligent sensing
  • flexible electrode
  • artificial intelligence

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Published Papers (3 papers)

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Research

16 pages, 1101 KiB  
Article
Enhancing Human–Robot Interaction: Development of Multimodal Robotic Assistant for User Emotion Recognition
by Sergio Garcia, Francisco Gomez-Donoso and Miguel Cazorla
Appl. Sci. 2024, 14(24), 11914; https://doi.org/10.3390/app142411914 - 19 Dec 2024
Viewed by 2665
Abstract
This paper presents a study on enhancing human–robot interaction (HRI) through multimodal emotional recognition within social robotics. Using the humanoid robot Pepper as a testbed, we integrate visual, auditory, and textual analysis to improve emotion recognition accuracy and contextual understanding. The proposed framework [...] Read more.
This paper presents a study on enhancing human–robot interaction (HRI) through multimodal emotional recognition within social robotics. Using the humanoid robot Pepper as a testbed, we integrate visual, auditory, and textual analysis to improve emotion recognition accuracy and contextual understanding. The proposed framework combines pretrained neural networks with fine-tuning techniques tailored to specific users, demonstrating that high accuracy in emotion recognition can be achieved by adapting the models to the individual emotional expressions of each user. This approach addresses the inherent variability in emotional expression across individuals, making it feasible to deploy personalized emotion recognition systems. Our experiments validate the effectiveness of this methodology, achieving high precision in multimodal emotion recognition through fine-tuning, while maintaining adaptability in real-world scenarios. These enhancements significantly improve Pepper’s interactive and empathetic capabilities, allowing it to engage more naturally with users in assistive, educational, and healthcare settings. This study not only advances the field of HRI but also provides a reproducible framework for integrating multimodal emotion recognition into commercial humanoid robots, bridging the gap between research prototypes and practical applications. Full article
(This article belongs to the Special Issue New Insights into Rehabilitation Robots with Intelligent Sensing Systems)
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16 pages, 3163 KiB  
Article
Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
by Ting Lan, Yanan Bie, Dong Hai and Jun Zhong
Appl. Sci. 2024, 14(24), 11631; https://doi.org/10.3390/app142411631 - 12 Dec 2024
Viewed by 846
Abstract
In order to address the issue of heart rate susceptibility to motion artifacts (MAs) when extracting it from photoplethysmography (PPG) signals, a heart rate estimation algorithm based on the finite state machine (FSM) is proposed. The algorithm first applies band-pass filtering to the [...] Read more.
In order to address the issue of heart rate susceptibility to motion artifacts (MAs) when extracting it from photoplethysmography (PPG) signals, a heart rate estimation algorithm based on the finite state machine (FSM) is proposed. The algorithm first applies band-pass filtering to the PPG and three-axis acceleration signals. The strength of MA is assessed based on the acceleration data. If a strong MA is detected, recursive least squares (RLS) filtering is applied; otherwise, it is omitted. Then, the signal is subjected to an empirical wavelet transform (EWT). Based on the EWT results, the current state is identified, and the corresponding spectral peak screening method is selected to estimate the heart rate. The mean absolute errors of the algorithm on 12 sets of public data and 8 sets of testing data are 0.93 and 1.76 beats per minute (bpm), respectively. The results of the experiment show that compared with other dominant algorithms, the proposed algorithm estimates heart rate with a smaller mean absolute error and can extract heart rate more effectively. Full article
(This article belongs to the Special Issue New Insights into Rehabilitation Robots with Intelligent Sensing Systems)
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20 pages, 6078 KiB  
Article
A Smart Motor Rehabilitation System Based on the Internet of Things and Humanoid Robotics
by Yasamin Moghbelan, Alfonso Esposito, Ivan Zyrianoff, Giulia Spaletta, Stefano Borgo, Claudio Masolo, Fabiana Ballarin, Valeria Seidita, Roberto Toni, Fulvio Barbaro, Giusy Di Conza, Francesca Pia Quartulli and Marco Di Felice
Appl. Sci. 2024, 14(24), 11489; https://doi.org/10.3390/app142411489 - 10 Dec 2024
Cited by 2 | Viewed by 1473
Abstract
The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context [...] Read more.
The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context of motor rehabilitation for groups of patients performing moderate physical routines, focused on balance, stretching, and posture. Specifically, we propose the I-TROPHYTS framework, which introduces a step-change in motor rehabilitation by advancing towards more sustainable medical services and personalized diagnostics. Our framework leverages wearable sensors to monitor patients’ vital signs and edge computing to detect and estimate motor routines. In addition, it incorporates a humanoid robot that mimics the actions of a physiotherapist, adapting motor routines in real-time based on the patient’s condition. All data from physiotherapy sessions are modeled using an ontology, enabling automatic reasoning and planning of robot actions. In this paper, we present the architecture of the proposed framework, which spans four layers, and discuss its enabling components. Furthermore, we detail the current deployment of the IoT system for patient monitoring and automatic identification of motor routines via Machine Learning techniques. Our experimental results, collected from a group of volunteers performing balance and stretching exercises, demonstrate that we can achieve nearly 100% accuracy in distinguishing between shoulder abduction and shoulder flexion, using Inertial Measurement Unit data from wearable IoT devices placed on the wrist and elbow of the test subjects. Full article
(This article belongs to the Special Issue New Insights into Rehabilitation Robots with Intelligent Sensing Systems)
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