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Applied Sciences
Special Issues
Applications of Robotics in Disease Treatment and Rehabilitation
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Applications of Robotics in Disease Treatment and Rehabilitation

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 January 2024) | Viewed by 3248

Special Issue Editor

Dr. Wei Wei

E-Mail Website
Guest Editor
College of Optoelectronics Science and Engineering, Soochow University, Suzhou 215000, China
Interests: exoskeleton robots; medical robots; intelligent sensors

Special Issue Information

Dear Colleagues,

In the past few years, many new technologies such as micro-sensors and machine learning have been greatly developed, which also contributed to the integration and intelligence of robots. With the development of the aging population, the demand for medical robots has risen sharply in the field of medical treatment and rehabilitation. Its goal is to replace the surgeon to perform simple and fast surgery, improve the quality of surgery and reduce the operation time. The rehabilitation robot can also provide rehabilitation assistance for patients and help them recover.

The contribution of this Special Issue "Applications of Robotics in Disease Treatment and Rehabilitation" aims to reflect the development of this field, covering a series of topics, including but not limited to system and algorithm development, theory, engineering, computational simulation, and experimental and clinical applications of robots in the following fields：

  • Rehabilitation robots;
  • Medical robots;
  • Intelligent prosthetics;
  • Human–robot interaction; 
  • Wearable sensors.

Dr. Wei Wei
Guest Editor

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

  • medical robots
  • wearable robot systems
  • rehabilitation robots
  • human assistance robots
  • ergonomics
  • human–robot interaction

Published Papers (4 papers)

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Research

20 pages, 4126 KiB  
Article
Sensorimotor Control Using Adaptive Neuro-Fuzzy Inference for Human-Like Arm Movement
by Gokhan Gungor and Mehdi Afshari
Appl. Sci. 2024, 14(7), 2974; https://doi.org/10.3390/app14072974 - 1 Apr 2024
Viewed by 521
Abstract
In this study, a sensorimotor controller is designed to characterize the required muscle force to enable a robotics system to perform a human-like circular movement. When the appropriate muscle internal forces are chosen, the arm end-point tracks the desired path via joint-space feedback. [...] Read more.
In this study, a sensorimotor controller is designed to characterize the required muscle force to enable a robotics system to perform a human-like circular movement. When the appropriate muscle internal forces are chosen, the arm end-point tracks the desired path via joint-space feedback. An objective function of the least-change rate of muscle forces is determined to find suitable feedback gains. The parameter defining the muscle force is then treated as a learning parameter through an adaptive neuro-fuzzy inference system, incorporating the rate of change of muscle forces. In experimental section, the arm motion of healthy subjects is captured using the inertial measurement unit sensors, and then the image of the drawn path is processed. The inertial measurement unit sensors detect each segment motion’s orientation using quaternions, and the image is employed to identify the exact end-point position. Experimental data on arm movement are then utilized in the control parameter computation. The proposed brain–motor control mechanism enhances motion performance, resulting in a more human-like movement. Full article
(This article belongs to the Special Issue Applications of Robotics in Disease Treatment and Rehabilitation)
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24 pages, 9458 KiB  
Article
Search for Optimal Parameters in the Control Structure of a Surgical System for Soft Tissue Operations Based on In Vitro Experiments on Cardiovascular Tissue
by Grzegorz Ilewicz and Edyta Ładyżyńska-Kozdraś
Appl. Sci. 2024, 14(6), 2551; https://doi.org/10.3390/app14062551 - 18 Mar 2024
Viewed by 528
Abstract
The surgical robots currently used in cardiac surgery are equipped with a remote center of motion (RCM) mechanism that enables the required spherical workspace. The dynamics model of the surgical robot’s RCM mechanism presented in this work includes a direct current (DC) motor, [...] Read more.
The surgical robots currently used in cardiac surgery are equipped with a remote center of motion (RCM) mechanism that enables the required spherical workspace. The dynamics model of the surgical robot’s RCM mechanism presented in this work includes a direct current (DC) motor, an optimal proportional–integral–derivative (PID) controller, and a LuGre friction model that takes into account the Stribeck effect and surface deformation. A finite element method (FEM) analysis of transients was carried out using the energy hypothesis of von Mises with an optimal input signal from the mechatronic system with a PID controller obtained using the Runge–Kutta differentiation method in the Dormand–Prince ordinary differential equations variant (ODE45). Five criteria were adopted for the objective function: the safety factor related to the stress function in the time-varying strength problem, the first natural frequency related to stiffness and the resonance phenomenon, the buckling coefficient in the statics problem related to stability, the static factor of safety, and the displacement of the operating tip. The force inputs to the dynamics model were derived from in vitro force measurements on cardiovascular tissue using a force sensor. The normality of the statistical distribution of the experimental data was confirmed using the Kolmogorov–Smirnov statistical test. The problem of multi-criteria optimization was solved using the non-sorter genetic algorithm (NSGA-II), the finite element method, and the von Mises distortion energy hypothesis. Velocity input signals for the transient dynamics model were obtained from a second in vitro experiment on cardiovascular tissue using the minimally robotic invasive surgery (MIRS) technique. An experienced cardiac surgeon conducted the experiment in a modern method using the Robin Heart Vision surgical robot, and a system of four complementary metal–oxide–semiconductor (CMOS) optical sensors and ariel performance analysis system (APAS-XP 2002) software were used to obtain the endoscopic tool trajectory signal. The trajectory signal was accurate to ±2 [mm] in relation to the adopted standard, and it was smoothed using the Savitzky–Golay (SG) polynomial smoothing, whose parameters were optimally selected using the Durbin–Watson (DW) statistical test. Full article
(This article belongs to the Special Issue Applications of Robotics in Disease Treatment and Rehabilitation)
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16 pages, 476 KiB  
Article
Nonlinear-Observer-Based Neural Fault-Tolerant Control for a Rehabilitation Exoskeleton Joint with Electro-Hydraulic Actuator and Error Constraint
by Changlin Song and Yong Yang
Appl. Sci. 2023, 13(14), 8294; https://doi.org/10.3390/app13148294 - 18 Jul 2023
Cited by 1 | Viewed by 652
Abstract
The rehabilitation exoskeleton is an effective piece of equipment for stroke patients and the aged. However, this complex human–robot system incurs many problems, such as modeling uncertainties, unknown human–robot interaction, external disturbance, and actuator fault. This paper addresses the adaptive fault-tolerant tracking control [...] Read more.
The rehabilitation exoskeleton is an effective piece of equipment for stroke patients and the aged. However, this complex human–robot system incurs many problems, such as modeling uncertainties, unknown human–robot interaction, external disturbance, and actuator fault. This paper addresses the adaptive fault-tolerant tracking control for a lower limb rehabilitation exoskeleton joint driven by an electro-hydraulic actuator (EHA). First, the model of the exoskeleton joint is built by considering the principle of the hydraulic cylinder and the servo valve. Then, a novel disturbance-observer-based neural fault-tolerant control scheme is proposed, where the neural network and disturbance observer are incorporated to reduce the influence of the the nonlinear uncertainties and disturbance. Meanwhile, a barrier Lyapunov function is constructed to ensure the stability of the closed-loop system. Finally, comparative simulations on an exoskeleton joint validate the effect of the proposed control scheme. Full article
(This article belongs to the Special Issue Applications of Robotics in Disease Treatment and Rehabilitation)
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13 pages, 4038 KiB  
Article
An Interventional Surgical Robot Based on Multi-Data Detection
by Dong Yang, Nan Xiao, Yuxuan Xia and Wei Wei
Appl. Sci. 2023, 13(9), 5301; https://doi.org/10.3390/app13095301 - 24 Apr 2023
Viewed by 1239
Abstract
Vascular interventional surgery is the most common method for the treatment of cardiovascular diseases. Interventional surgical robot has attracted extensive attention because of its precise control and remote operation. However, conventional force sensors in surgical robots can only detect the axial thrust pressure [...] Read more.
Vascular interventional surgery is the most common method for the treatment of cardiovascular diseases. Interventional surgical robot has attracted extensive attention because of its precise control and remote operation. However, conventional force sensors in surgical robots can only detect the axial thrust pressure of the catheter. Inspired by the function of insect antennae, we designed a structure with a thin-film force sensing device in the catheter head. Combined with the pressure sensor in the catheter clamping device, multiple sensor data were fused to predict and classify the current vascular environment using the LSTM network with 94.2% accuracy. During robotic surgery, real-time feedback of current pressure information and vascular curvature information can enhance doctors’ judgment of surgical status and improve surgical safety. Full article
(This article belongs to the Special Issue Applications of Robotics in Disease Treatment and Rehabilitation)
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