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Electronics
Special Issues
Recent Advanced Applications of Rehabilitation and Medical Robotics
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Recent Advanced Applications of Rehabilitation and Medical Robotics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 13256

Special Issue Editors

Prof. Dr. Pengcheng Liu

E-Mail Website1 Website2
Guest Editor
Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK
Interests: robotics; bio-inspired and soft robotics; robot learning; rehabilitation and medical robotics; human-adaptive cyber-physical systems; nonlinear dynamical systems; adaptive control; autonomous systems; machine learning; computational learning
Prof. Dr. Guibin Bian

E-Mail Website1 Website2
Guest Editor
Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing 100190, China
Interests: design, sensing and control for medical robotics
Dr. Anwar P.P. Abdul Majeed

E-Mail Website
Guest Editor
Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
Interests: computational mechanics; sports engineering; control dynamics and vibration; machine learning; deep learning
Prof. Dr. Ahmad Athif

E-Mail Website
Guest Editor
School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai 81310, Johor, Malaysia
Interests: intelligent actuators; embedded system; automation control; system integration; soft mechanism

Special Issue Information

Dear Colleagues,

Robotic technologies are able to provide precise and accurate sensing and movement capabilities. Recently, advances in the applications of rehabilitation and medical robots have led to powerful insights about the nature of understanding and augmenting rehabilitation and medical treatments. However, new and challenging theoretical and technological problems are being posed. One can apply state-of-the-art robotic technologies in different ways, including robot-assisted surgery, upper and lower limb rehabilitation, cognitive computation, behavioural intelligence and statistics. Such convergence of interests is encouraging, but few researchers in this active area communicate across disciplinary boundaries, and even fewer are skilled in the ‘language’ and techniques of more than one approach. With this new era of rehabilitation and medical robotics, much research is needed in order to continue to advance the field and also to evaluate the multidisciplinary concerns of the existing robotics and learning techniques.

The main aim of this Special Issue is to seek high-quality submissions that highlight emerging applications and address recent breakthroughs in medical and rehabilitation robotics. The topics of interest include, but are not limited to:

  • Robot-assisted surgery;
  • Upper and lower limb rehabilitation;
  • Medical image analysis;
  • EEG/sEMG data analytics;
  • Stroke rehabilitation;
  • Brain-computer interfaces;
  • Haptic sensing;
  • Neuro-imaging based diagnosis;
  • Neural image captioning;
  • Cognitive computation for healthcare;
  • Targeted neuroplasticity;
  • Human activity recognition;
  • Extraction and optimisation techniques;
  • Human-machine interfaces/integrations;
  • Humanoid service robotics;
  • Teleoperation/ telerobotics;
  • Medical devices and instrumentation;
  • Mobile healthcare;
  • Rehabilitation games.

Prof. Dr. Pengcheng Liu
Prof. Dr. Guibin Bian
Dr. Anwar P.P. Abdul Majeed
Prof. Dr. Ahmad Athif
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. Electronics 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

  • Robot-assisted surgery
  • Upper and lower limb rehabilitation
  • Medical image analysis
  • EEG/sEMG data analytics
  • Stroke rehabilitation
  • Brain-computer interfaces
  • Haptic sensing
  • Neuro-imaging based diagnosis
  • Neural image captioning
  • Cognitive computation for healthcare
  • Targeted neuroplasticity
  • Human activity recognition
  • Extraction and optimisation techniques
  • Human-machine interfaces/integrations
  • Humanoid service robotics
  • Teleoperation/ telerobotics
  • Medical devices and instrumentation
  • Mobile healthcare
  • Rehabilitation games

Published Papers (6 papers)

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Research

12 pages, 3914 KiB  
Article
DEF-Net: A Dual-Encoder Fusion Network for Fundus Retinal Vessel Segmentation
by Jianyong Li, Ge Gao, Lei Yang, Yanhong Liu and Hongnian Yu
Electronics 2022, 11(22), 3810; https://doi.org/10.3390/electronics11223810 - 19 Nov 2022
Cited by 4 | Viewed by 1423
Abstract
The deterioration of numerous eye diseases is highly related to the fundus retinal structures, so the automatic retinal vessel segmentation serves as an essential stage for efficient detection of eye-related lesions in clinical practice. Segmentation methods based on encode-decode structures exhibit great potential [...] Read more.
The deterioration of numerous eye diseases is highly related to the fundus retinal structures, so the automatic retinal vessel segmentation serves as an essential stage for efficient detection of eye-related lesions in clinical practice. Segmentation methods based on encode-decode structures exhibit great potential in retinal vessel segmentation tasks, but have limited feature representation ability. In addition, they don’t effectively consider the information at multiple scales when performing feature fusion, resulting in low fusion efficiency. In this paper, a newly model, named DEF-Net, is designed to segment retinal vessels automatically, which consists of a dual-encoder unit and a decoder unit. Fused with recurrent network and convolution network, a dual-encoder unit is proposed, which builds a convolutional network branch to extract detailed features and a recurrent network branch to accumulate contextual features, and it could obtain richer features compared to the single convolution network structure. Furthermore, to exploit the useful information at multiple scales, a multi-scale fusion block used for facilitating feature fusion efficiency is designed. Extensive experiments have been undertaken to demonstrate the segmentation performance of our proposed DEF-Net. Full article
(This article belongs to the Special Issue Recent Advanced Applications of Rehabilitation and Medical Robotics)
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17 pages, 2087 KiB  
Article
Force Tracking Control of Functional Electrical Stimulation via Hybrid Active Disturbance Rejection Control
by Benyan Huo, Ruishun Wang, Yunhui Qin, Zhenlong Wu, Guibin Bian and Yanhong Liu
Electronics 2022, 11(11), 1727; https://doi.org/10.3390/electronics11111727 - 30 May 2022
Viewed by 1608
Abstract
Stroke is a worldwide disease with a high incidence rate. After surviving a stroke, most patients are left with impaired upper or lower limb. Muscle force training is vital for stroke patients to recover limb function and improve their quality of life. This [...] Read more.
Stroke is a worldwide disease with a high incidence rate. After surviving a stroke, most patients are left with impaired upper or lower limb. Muscle force training is vital for stroke patients to recover limb function and improve their quality of life. This paper proposes a force tracking control method for upper limb based on functional electrical stimulation (FES), which is a promising rehabilitation approach. A modified Hammerstein model is proposed to describe the nonlinear dynamics of biceps brachii, which consists of a nonlinear mapping function, linear dynamics and time delay component to represent the biochemical process of muscle contraction. A quick model identification method is presented based on the least square algorithm. To deal with the variation of muscle dynamics, a hybrid active disturbance rejection control (ADRC) is proposed to estimate and compensate for the model uncertainty and unmeasured disturbances. The parameter tuning process is given. In the end, the performance of the proposed methods is verified via simulations and experiments. Compared with the Proportional integral derivative controller (PID) method, the proposed methods could suppress the model uncertainty and improve the tracking precision. Full article
(This article belongs to the Special Issue Recent Advanced Applications of Rehabilitation and Medical Robotics)
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11 pages, 4173 KiB  
Article
Efficient Ring-Topology Decentralized Federated Learning with Deep Generative Models for Medical Data in eHealthcare Systems
by Zhao Wang, Yifan Hu, Shiyang Yan, Zhihao Wang, Ruijie Hou and Chao Wu
Electronics 2022, 11(10), 1548; https://doi.org/10.3390/electronics11101548 - 12 May 2022
Cited by 9 | Viewed by 2203
Abstract
By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go [...] Read more.
By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go healthcare services. Although many federated learning (FL) approaches have been proposed with DNNs for medical applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topology-based decentralized federated learning (RDFL) scheme for deep generative models (DGM), where DGM is a promising solution for solving the aforementioned data usability issues. Our RDFL schemes provide communication efficiency and maintain training performance to boost DGMs in target tasks compared with existing FL works. A novel ring FL topology and a map-reduce-based synchronizing method are designed in the proposed RDFL to improve the decentralized FL performance and bandwidth utilization. In addition, an inter-planetary file system (IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstrate the superiority of RDFL with either independent and identically distributed (IID) datasets or non-independent and identically distributed (Non-IID) datasets. Full article
(This article belongs to the Special Issue Recent Advanced Applications of Rehabilitation and Medical Robotics)
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14 pages, 5777 KiB  
Article
Self-Oxygen Regulator System for COVID-19 Patients Based on Body Weight, Respiration Rate, and Blood Saturation
by Indrarini Dyah Irawati, Sugondo Hadiyoso, Akhmad Alfaruq, Atik Novianti and Achmad Rizal
Electronics 2022, 11(9), 1380; https://doi.org/10.3390/electronics11091380 - 26 Apr 2022
Cited by 2 | Viewed by 2266
Abstract
One of the symptoms that appears in patients with COVID-19 is hypoxia or a lack of oxygen in the body’s tissues or cells below the proper level. One of the methods used to treat hypoxia is to provide oxygen to the patient. Another [...] Read more.
One of the symptoms that appears in patients with COVID-19 is hypoxia or a lack of oxygen in the body’s tissues or cells below the proper level. One of the methods used to treat hypoxia is to provide oxygen to the patient. Another device that is needed in oxygen therapy for the patient is an oxygen regulator. An oxygen regulator is needed to regulate the volume of oxygen released to the patient. Currently, the control of oxygen flow by the regulator is still done manually. Therefore, in this study, an oxygen regulator was designed that has the ability to regulate the volume of oxygen output based on body weight, respiration rate, and blood saturation. Using these three parameters, the volume of oxygen to be released is adjusted according to the patient’s needs. The system consists of a temperature sensor, mlx90614, and an oxygen saturation sensor, Max30102. The data from the two sensors are processed using microcontrollers to control the movement of the stepper motor as a regulator of the oxygen output volume. The test results show that the system can control the oxygen regulator automatically with a delta error of 0.5–1 L/min. This device is expected to be used for COVID-19 patients who are undergoing self-isolation or who are outpatients. Full article
(This article belongs to the Special Issue Recent Advanced Applications of Rehabilitation and Medical Robotics)
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15 pages, 1105 KiB  
Article
Robust Hand Gesture Recognition Using HOG-9ULBP Features and SVM Model
by Jianyong Li, Chengbei Li, Jihui Han, Yuefeng Shi, Guibin Bian and Shuai Zhou
Electronics 2022, 11(7), 988; https://doi.org/10.3390/electronics11070988 - 23 Mar 2022
Cited by 13 | Viewed by 3428
Abstract
Hand gesture recognition is an area of study that attempts to identify human gestures through mathematical algorithms, and can be used in several fields, such as communication between deaf-mute people, human–computer interaction, intelligent driving, and virtual reality. However, changes in scale and angle, [...] Read more.
Hand gesture recognition is an area of study that attempts to identify human gestures through mathematical algorithms, and can be used in several fields, such as communication between deaf-mute people, human–computer interaction, intelligent driving, and virtual reality. However, changes in scale and angle, as well as complex skin-like backgrounds, make gesture recognition quite challenging. In this paper, we propose a robust recognition approach for multi-scale as well as multi-angle hand gestures against complex backgrounds. First, hand gestures are segmented from complex backgrounds using the single Gaussian model and K-means algorithm. Then, the HOG feature and an improved 9ULBP feature are fused into the HOG-9ULBP feature, which is invariant in scale and rotation and enables accurate feature extraction. Finally, SVM is adopted to complete the hand gesture classification. Experimental results show that the proposed method achieves the highest accuracy of 99.01%, 97.50%, and 98.72% on the self-collected dataset, the NUS dataset, and the MU HandImages ASL dataset, respectively. Full article
(This article belongs to the Special Issue Recent Advanced Applications of Rehabilitation and Medical Robotics)
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26 pages, 18513 KiB  
Article
HMM-Based Dynamic Mapping with Gaussian Random Fields
by Hongjun Li, Miguel Barão, Luís Rato and Shengjun Wen
Electronics 2022, 11(5), 722; https://doi.org/10.3390/electronics11050722 - 26 Feb 2022
Cited by 1 | Viewed by 1262
Abstract
This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which [...] Read more.
This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments. Full article
(This article belongs to the Special Issue Recent Advanced Applications of Rehabilitation and Medical Robotics)
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