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Human-Robot Interaction Based on Rehabilitation Sensing and Signal Processing
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Human-Robot Interaction Based on Rehabilitation Sensing and Signal Processing

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

Deadline for manuscript submissions: 5 April 2025 | Viewed by 13935

Special Issue Editors

Prof. Dr. Wei Meng

E-Mail Website
Guest Editor
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: rehabilitation robot; soft actuators; wearable sensors; human–robot interaction; adaptive control; bio-mechatronics
Prof. Dr. Tianliang Li

E-Mail Website
Guest Editor
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: optial fiber sensing technology; minimally invasive surgical robot; force sensor; force measurement; health monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenhong Li

E-Mail Website
Guest Editor
School of Electronic and Electrical Engineering, University of Leeds, Leeds, UK
Interests: rehabilitation robot; physical human–robot interaction; distributed sensor network and optimization; event-triggered control

Special Issue Information

Dear Colleagues,

Over the last decade, there has been an increased amount of research into the use of bio-sensing and signal processing in rehabilitation due to the increasing number of elderly and disabled people with neural diseases, such as stroke, worldwide. The emerging signal sensing and processing technologies are crucial in the design and development of intelligent rehabilitation devices and interactive control systems. Rapid advances in soft actuators, wearable sensors and robots or exoskeletons, as well as human-in-the-loop control of such soft mechatronic systems, in the last several years, have demonstrated the growing significance and potential utility of this unique advantage in the rehabilitation practice. This Special Issue aims to attract experts from all around the world to provide an overview of the current research and developments in the innovative technologies for advanced human–robot interaction technologies based on rehabilitation sensing and signal processing. Contributions addressing the state-of-the-art developments and methodologies, as well as the applications of bio-sensing, processing and control technologies and perspectives on the future are all welcomed. The topics include, but are not limited to, wearable sensing, soft actuating, advanced materials, prosthetics and orthotics, exoskeletons and intelligent control, transparent human–robot interaction, etc.

Manuscripts should contain both theoretical and practical/experimental results. Potential topics include, but are not limited to, the following:

  • Soft sensing and wearable sensors, robotics and exoskeletons for rehabilitation;
  • Signal processing, health monitoring and rehabilitation assessment;
  • Compliant human–robot interaction and intelligent control;
  • Applications and clinical practices of bio-mechatronic technologies.

Prof. Dr. Wei Meng
Prof. Dr. Tianliang Li
Dr. Zhenhong Li
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. 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.

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Published Papers (5 papers)

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Research

16 pages, 2873 KiB  
Article
Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals
by Katarzyna Klęczek, Andra Rice and Maryam Alimardani
Sensors 2024, 24(13), 4032; https://doi.org/10.3390/s24134032 - 21 Jun 2024
Cited by 2 | Viewed by 2718
Abstract
The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic Skin Responses (GSRs) together with self-reported questionnaires from two groups of students who [...] Read more.
The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic Skin Responses (GSRs) together with self-reported questionnaires from two groups of students who practiced a deep breathing exercise either with a social robot or a laptop. From GSR signals, we obtained the change in participants’ arousal level throughout the intervention, and from the EEG signals, we extracted the change in their emotional valence using the neurometric of Frontal Alpha Asymmetry (FAA). While subjective perceptions of stress and user experience did not differ significantly between the two groups, the physiological signals revealed differences in their emotional responses as evaluated by the arousal–valence model. The Laptop group tended to show a decrease in arousal level which, in some cases, was accompanied by negative valence indicative of boredom or lack of interest. On the other hand, the Robot group displayed two patterns; some demonstrated a decrease in arousal with positive valence indicative of calmness and relaxation, and others showed an increase in arousal together with positive valence interpreted as excitement. These findings provide interesting insights into the impact of social robots as mental well-being coaches on students’ emotions particularly in the presence of the novelty effect. Additionally, they provide evidence for the efficacy of physiological signals as an objective and reliable measure of user experience in HRI settings. Full article
(This article belongs to the Special Issue Human-Robot Interaction Based on Rehabilitation Sensing and Signal Processing)
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13 pages, 5647 KiB  
Article
The Influence of Mobile Device Type on Camera-Based Monitoring of Neck Movements for Cervical Rehabilitation
by Maria Francesca Roig-Maimó, Iosune Salinas-Bueno, Ramon Mas-Sansó, Javier Varona and Pau Martínez-Bueso
Sensors 2023, 23(5), 2482; https://doi.org/10.3390/s23052482 - 23 Feb 2023
Cited by 2 | Viewed by 1974
Abstract
We developed a mobile application for cervical rehabilitation that uses a non-invasive camera-based head-tracker sensor for monitoring neck movements. The intended user population should be able to use the mobile application in their own mobile device, but mobile devices have different camera sensors [...] Read more.
We developed a mobile application for cervical rehabilitation that uses a non-invasive camera-based head-tracker sensor for monitoring neck movements. The intended user population should be able to use the mobile application in their own mobile device, but mobile devices have different camera sensors and screen dimensions that could affect the user performance and neck movement monitoring. In this work, we studied the influence of mobile devices type on camera-based monitoring of neck movements for rehabilitation purposes. We conducted an experiment to test whether the characteristics of a mobile device affect neck movements when using the mobile application with the head-tracker. The experiment consisted of the use of our application, containing an exergame, in three mobile devices. We used wireless inertial sensors to measure the real-time neck movements performed while using the different devices. The results showed that the effect of device type on neck movements was not statistically significant. We included the sex factor in the analysis, but there was no statistically significant interaction between sex and device variables. Our mobile application proved to be device-agnostic. This will allow intended users to use the mHealth application regardless of the type of device. Thus, future work can continue with the clinical evaluation of the developed application to analyse the hypothesis that the use of the exergame will improve therapeutic adherence in cervical rehabilitation. Full article
(This article belongs to the Special Issue Human-Robot Interaction Based on Rehabilitation Sensing and Signal Processing)
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13 pages, 2184 KiB  
Article
Mobility Analysis of the Lumbar Spine with a Dynamic Spine-Correction Device
by Wojciech Kaczmarek, Łukasz Pulik, Paweł Łęgosz and Krzysztof Mucha
Sensors 2023, 23(4), 1940; https://doi.org/10.3390/s23041940 - 9 Feb 2023
Viewed by 2867
Abstract
According to data, 60–70% of the world’s population experience low-back pain (LBP) at least once during their lifetime, often at a young or middle age. Those affected are at risk of having worse quality of life, more missed days at work, and higher [...] Read more.
According to data, 60–70% of the world’s population experience low-back pain (LBP) at least once during their lifetime, often at a young or middle age. Those affected are at risk of having worse quality of life, more missed days at work, and higher medical care costs. We present a new rehabilitation method that helps collect and analyze data on an ongoing basis and offers a more personalized therapeutic approach. This method involves assessing lumbar spine rotation (L1–L5) during torso movement using an innovative dynamic spine correction (DSC) device designed for postural neuromuscular reeducation in LBP. Spinal mobility was tested in 54 patients (aged 18 to 40 years) without LBP. Measurements were made with 12-bit rotary position sensors (AS5304) of the DSC device. During exercise, the mean lumbar spine rotation to the right was greater (4.78° ± 2.24°) than that to the left (2.99° ± 1.44°; p < 0.001). Similarly, the maximum rotation to the right was greater (11.35° ± 3.33°) than that to the left (7.42° ± 1.44°; p < 0.0001). The measurements obtained in the study can serve as a reference for future therapeutic use of the device. Full article
(This article belongs to the Special Issue Human-Robot Interaction Based on Rehabilitation Sensing and Signal Processing)
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17 pages, 2564 KiB  
Article
A Universal Decoupled Training Framework for Human Parsing
by Yang Li, Huahong Zuo and Ping Han
Sensors 2022, 22(16), 5964; https://doi.org/10.3390/s22165964 - 9 Aug 2022
Cited by 1 | Viewed by 1938
Abstract
Human parsing is an important technology in human–robot interaction systems. At present, the distribution of multi-category human parsing datasets is unbalanced, and the samples present a long-tailed distribution, which directly affects the performance of human parsing. Meanwhile, the similarity between different categories leads [...] Read more.
Human parsing is an important technology in human–robot interaction systems. At present, the distribution of multi-category human parsing datasets is unbalanced, and the samples present a long-tailed distribution, which directly affects the performance of human parsing. Meanwhile, the similarity between different categories leads the model to predict false parsing results. To solve the above problems, a general decoupled training framework called Decoupled Training framework based on Pixel Resampling (DTPR) was proposed to solve the long-tailed distribution, and a new sampling method named Pixel Resampling based on Accuracy distribution (PRA) for semantic segmentation was also proposed and applied to this decoupled training framework. The framework divides the training process into two phases, the first phase is to improve the model feature extraction ability, and the second phase is to improve the performance of the model on tail categories. The training framework was evaluated in MHPv2.0 and LIP datasets, and tested in both high-precision and real-time SOTA models. The MPA metric of model trained by DTPR in above two datasets increased by more than 6%, and the mIoU metric increased by more than 1% without changing the model structure. Full article
(This article belongs to the Special Issue Human-Robot Interaction Based on Rehabilitation Sensing and Signal Processing)
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22 pages, 6664 KiB  
Article
Improving Haptic Response for Contextual Human Robot Interaction
by Stanley Mugisha, Vamsi Krisha Guda, Christine Chevallereau, Matteo Zoppi, Rezia Molfino and Damien Chablat
Sensors 2022, 22(5), 2040; https://doi.org/10.3390/s22052040 - 5 Mar 2022
Cited by 7 | Viewed by 2863
Abstract
For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location defined in the virtual environment in time. However, due to device limitations, delays are always unavoidable. One [...] Read more.
For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location defined in the virtual environment in time. However, due to device limitations, delays are always unavoidable. One of the solutions to improve the device response is to infer human intended motion and move the robot at the earliest time possible to the desired goal. This paper presents an experimental study to improve the prediction time and reduce the robot time taken to reach the desired position. We developed motion strategies based on the hand motion and eye-gaze direction to determine the point of user interaction in a virtual environment. To assess the performance of the strategies, we conducted a subject-based experiment using an exergame for reach and grab tasks designed for upper limb rehabilitation training. The experimental results in this study revealed that eye-gaze-based prediction significantly improved the detection time by 37% and the robot time taken to reach the target by 27%. Further analysis provided more insight on the effect of the eye-gaze window and the hand threshold on the device response for the experimental task. Full article
(This article belongs to the Special Issue Human-Robot Interaction Based on Rehabilitation Sensing and Signal Processing)
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