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Electronics
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Advanced Wearable/Flexible Devices and Systems in Bioelectronics
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Advanced Wearable/Flexible Devices and Systems in Bioelectronics

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

Deadline for manuscript submissions: 15 August 2024 | Viewed by 7657

Special Issue Editors

Dr. Wujing Cao

E-Mail Website
Guest Editor
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518005, China
Interests: lower limb exoskeletons; intelligent prosthesis; rehabilitation robotics; human–machine interaction
Dr. Qiaoling Meng

E-Mail Website
Guest Editor
Institute of Intelligent Rehabilitation Engineering of USST, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: soft exoskeletons; rehabilitation robotics; intelligent prosthesis
Dr. Yuquan Leng

E-Mail Website
Guest Editor
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: human-augmented robot; perception and navigation for robot; machine learning algorithm
Dr. Muye Pang

E-Mail Website
Guest Editor
School of Automation, Wuhan University of Technology, Wuhan 430070, China
Interests: exoskeleton robot; human-inspired control; biomechanics; surface electromyographic signal processing

Special Issue Information

Dear Colleagues,

Wearable systems have been widely applied to biomedical engineering in motion recognition, health monitoring, human ability rehabilitation, and augmentation. More and more novel wearable devices are developed with the progress of electronics. Exoskeletons, intelligent prosthesis, and rehabilitation robotics are typical wearable systems applied to biomedical engineering. To ensure the rapid development of biomedical engineering devices, wearable systems are a cutting-edge approach, but with several difficulties, requiring continued research focus. The purpose of the Special Issue is to share the latest wearable systems applied to biomedical engineering and promote the progress in wearable technology. This includes:

  • Wearable sensor systems applied to humans;
  • Upper and lower limb exoskeletons;
  • Intelligent prosthesis;
  • Human–machine interaction in biomedical engineering;
  • Motion intention recognition;
  • Biomedical signal processing;
  • Bio-inspired control;
  • Wearable electronics;
  • Wearable rehabilitation robotics;
  • Motion analysis by wearable systems;
  • Motion augmentation by wearable systems.

Dr. Wujing Cao
Dr. Qiaoling Meng
Dr. Yuquan Leng
Dr. Muye Pang
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

  • wearable devices
  • biomedical signal processing
  • human–machine interaction
  • wearable electronics
  • bio-inspired control

Published Papers (6 papers)

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Research

17 pages, 4864 KiB  
Article
Inertial Measurement Unit-Based Real-Time Adaptive Algorithm for Human Walking Pattern and Gait Event Detection
by Yinxiao Lu, Jun Zhu, Wenming Chen and Xin Ma
Electronics 2023, 12(20), 4319; https://doi.org/10.3390/electronics12204319 - 18 Oct 2023
Viewed by 787
Abstract
In this work, a lightweight adaptive hybrid gait detection method with two inertial measurement units (IMUs) on the foot and thigh was developed and preliminarily evaluated. An adaptive detection algorithm is used to eliminate the pre-training phase and to modify parameters according to [...] Read more.
In this work, a lightweight adaptive hybrid gait detection method with two inertial measurement units (IMUs) on the foot and thigh was developed and preliminarily evaluated. An adaptive detection algorithm is used to eliminate the pre-training phase and to modify parameters according to the changes within a walking trial using an adaptive two-level architecture. The present algorithm has a two-layer structure: a real-time detection algorithm for detecting the current gait pattern and events at 100 Hz., and a short-time online training layer for updating the parameters of gait models for each gait pattern. Three typical walking patterns, including level-ground walking (LGW), stair ascent (SA), and stair descent (SD), and four events/sub-phases of each pattern, can be detected on a portable Raspberry-Pi platform with two IMUs on the thigh and foot in real-time. A preliminary algorithm test was implemented with healthy subjects in common indoor corridors and stairs. The results showed that the on-board model training and event decoding processes took 20 ms and 1 ms, respectively. Motion detection accuracy was 97.8% for LGW, 95.6% for SA, and 97.1% for SD. F1-scores for event detection were over 0.86, and the maximum time delay was steadily below 51 ± 32.4 ms. Some of the events in gait models of SA and SD seemed to be correlated with knee extension and flexion. Given the simple and convenient hardware requirements, this method is suitable for knee assistive device applications. Full article
(This article belongs to the Special Issue Advanced Wearable/Flexible Devices and Systems in Bioelectronics)
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20 pages, 5231 KiB  
Article
Development of a Compliant Lower-Limb Rehabilitation Robot Using Underactuated Mechanism
by Yunlong Yang, Junlong Guo, Yufeng Yao and Hesheng Yin
Electronics 2023, 12(16), 3436; https://doi.org/10.3390/electronics12163436 - 14 Aug 2023
Viewed by 964
Abstract
Most existing lower-limb rehabilitation robots (LLRR) for stroke and postoperative rehabilitation are bulky and prone to misalignments between robot and human joints. These drawbacks hamper LLRR application, leading to poor arthro-kinematic compatibility. To address these challenges, this paper proposes a novel robot with [...] Read more.
Most existing lower-limb rehabilitation robots (LLRR) for stroke and postoperative rehabilitation are bulky and prone to misalignments between robot and human joints. These drawbacks hamper LLRR application, leading to poor arthro-kinematic compatibility. To address these challenges, this paper proposes a novel robot with portability and compliance features. The developed robot consists of an underactuated mechanism and a crus linkage, respectively corresponding to the hip and knee joints. The underactuated mechanism is a new type of remote center of motion (RCM) mechanism with two sets of contractible slider cranks that can reduce the misalignments between robot and human joints. The underactuated mechanism is then optimized using the particle swarm optimization method, and the developed robot’s kinematic analysis is presented. The proposed robot can be simplified as a two-link mechanism with the ability to easily plan its trajectory using the modified Denavit–Hartenberg method. Finally, passive exercise trials demonstrate that the mismatch angles between the human and robot knee joints are less than 2.1% of the range of motion, confirming the feasibility and effectiveness of the proposed robot. Full article
(This article belongs to the Special Issue Advanced Wearable/Flexible Devices and Systems in Bioelectronics)
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17 pages, 6049 KiB  
Article
Bionic Design of a Novel Portable Hand-Elbow Coordinate Exoskeleton for Activities of Daily Living
by Qingyun Meng, Guanxin Liu, Qiaoling Meng, Xin Xu, Liang Qin and Hongliu Yu
Electronics 2023, 12(15), 3326; https://doi.org/10.3390/electronics12153326 - 03 Aug 2023
Cited by 1 | Viewed by 1128
Abstract
This paper presents the mechanical design and test of a portable hand-elbow combination linkage upper limb rehabilitation robot, which can realize the joint movement of the hand joint and elbow joint and reproduce the complete grasping action. The joints that need bionic support [...] Read more.
This paper presents the mechanical design and test of a portable hand-elbow combination linkage upper limb rehabilitation robot, which can realize the joint movement of the hand joint and elbow joint and reproduce the complete grasping action. The joints that need bionic support are determined according to the characteristics of human upper limbs and hands, and the overall bionic mechanism is designed. The Motion module in SolidWorks is used to simulate and analyze the rehabilitation robot. The measurement experiment and grasping experiment of joint mobility are carried out on the experimental prototype. As a result, the angular displacement and linear displacement curves obtained via the simulation results are smooth. The measurement experiment of the joint range of motion confirms that the joint range of motion is also within the range of the normal joint angle of the human body, and the grasping experiment shows that the exoskeleton can grasp and lift a 1.801-kg cylindrical object and other daily necessities of different shapes. This result shows that the design of the portable hand-elbow combination linkage upper limb rehabilitation robot is reasonable, can satisfy the rehabilitation training requirements of the hand and upper limb, and has some ability to assist users in daily life. Full article
(This article belongs to the Special Issue Advanced Wearable/Flexible Devices and Systems in Bioelectronics)
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16 pages, 5131 KiB  
Article
Real-Time sEMG Pattern Recognition of Multiple-Mode Movements for Artificial Limbs Based on CNN-RNN Algorithm
by Sujiao Li, Yue Zhang, Yuanmin Tang, Wei Li, Wanjing Sun and Hongliu Yu
Electronics 2023, 12(11), 2444; https://doi.org/10.3390/electronics12112444 - 28 May 2023
Cited by 1 | Viewed by 1572
Abstract
Currently, sEMG-based pattern recognition is a crucial and promising control method for prosthetic limbs. A 1D convolutional recurrent neural network classification model for recognizing online finger and wrist movements in real time was proposed to address the issue that the classification recognition rate [...] Read more.
Currently, sEMG-based pattern recognition is a crucial and promising control method for prosthetic limbs. A 1D convolutional recurrent neural network classification model for recognizing online finger and wrist movements in real time was proposed to address the issue that the classification recognition rate and time delay cannot be considered simultaneously. This model could effectively combine the advantages of the convolutional neural network and recurrent neural network. Offline experiments were used to verify the recognition performance of 20 movements, and a comparative analysis was conducted with CNN and LSTM classification models. Online experiments via the self-developed sEMG signal pattern recognition system were established to examine real-time recognition performance and time delay. Experiment results demonstrated that the average recognition accuracy of the 1D-CNN-RNN classification model achieved 98.96% in offline recognition, which is significantly higher than that of the CNN and LSTM (85.43% and 96.88%, respectively, p < 0.01). In the online experiments, the average accuracy of the real-time recognition of the 1D-CNN-RNN reaches 91% ± 5%, and the average delay reaches 153 ms. The proposed 1D-CNN-RNN classification model illustrates higher performances in real-time recognition accuracy and shorter time delay with no obvious sense of delay in the human body, which is expected to be an efficient control for dexterous prostheses. Full article
(This article belongs to the Special Issue Advanced Wearable/Flexible Devices and Systems in Bioelectronics)
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18 pages, 4844 KiB  
Article
Stability Study of an Interventional Surgery Robot Based on Active Disturbance Rejection Control
by Xu Ma and Quan Wen
Electronics 2023, 12(9), 2115; https://doi.org/10.3390/electronics12092115 - 05 May 2023
Cited by 1 | Viewed by 1176
Abstract
Interventional surgery robots are essential in cardiovascular surgery as remote medical devices. By performing remote surgery, surgeons can reduce surgical fatigue and after-effects from heavy surgical instruments and radiation, ensuring that cardiovascular surgery is performed in a safe and reliable manner. To enhance [...] Read more.
Interventional surgery robots are essential in cardiovascular surgery as remote medical devices. By performing remote surgery, surgeons can reduce surgical fatigue and after-effects from heavy surgical instruments and radiation, ensuring that cardiovascular surgery is performed in a safe and reliable manner. To enhance stability during interventional procedures and reduce the impact of surgical risk due to factors where the robotic guidewire section from the end is vulnerable to mechanical jitter or blockage by blood flow, lipids, and thrombus inside the vessel, a new control method is proposed. The active disturbance rejection controller (ADRC) combined with intelligence algorithms is used to improve the performance of the controller by introducing the fuzzy inference algorithm and RBF neural network algorithm to self-adjust the parameters of the controller so that it has a greater ability to compensate for the disturbance factors appearing in the system. In numerical simulation experiments, the advantages and disadvantages of the ADRC combined with intelligence algorithms and the control performance of the conventional control strategy are analyzed in terms of the following: disturbance suppression performance and flexibility performance, respectively. Finally, different types of working conditions have been designed in the experimental platform to simulate the operation flow of in vivo vascular surgery. Experimental results show that the controller proposed in this paper meets the high accuracy, fast response, and low deviation required by interventional vascular surgery robots in complex surgical environments and can provide a more reliable guarantee for the stability of interventional surgery robots. Full article
(This article belongs to the Special Issue Advanced Wearable/Flexible Devices and Systems in Bioelectronics)
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14 pages, 4237 KiB  
Article
Research on Posture Sensing and Error Elimination for Soft Manipulator Using FBG Sensors
by Wenyu Li, Yanlin He, Peng Geng and Yi Yang
Electronics 2023, 12(6), 1476; https://doi.org/10.3390/electronics12061476 - 21 Mar 2023
Cited by 1 | Viewed by 1133
Abstract
Fiber-optic sensors are highly promising within soft robot sensing applications, but sensing methods based on geometry-based reconstruction limit the sensing capability and range. In this study, a fiber-optic sensor with a different deployment strategy for indirect sensing to monitor the outside posture of [...] Read more.
Fiber-optic sensors are highly promising within soft robot sensing applications, but sensing methods based on geometry-based reconstruction limit the sensing capability and range. In this study, a fiber-optic sensor with a different deployment strategy for indirect sensing to monitor the outside posture of a soft manipulator is presented. The internal support structure’s curvature was measured using the FBG sensor, and its mapping to the external pose was then modelled using a modified LSTM network. The error was assumed to follow the Gaussian distribution in the LSTM neural network and was rectified by maximum likelihood estimation to address the issue of noise generated during the deformation transfer and curvature sensing of the soft structure. For the soft manipulator, the network model’s sensing performance was demonstrated. The proposed method’s average absolute error for posture sensing was 63.3% lower than the error before optimization, and the root mean square error was 56.9% lower than the error before optimization. The comparison results between the experiment and the simulation demonstrate the viability of the indirect measurement of the soft structure posture using FBG sensors based on the data-driven method, as well as the significant impact of the error optimization method based on the Gaussian distribution assumption. Full article
(This article belongs to the Special Issue Advanced Wearable/Flexible Devices and Systems in Bioelectronics)
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