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Medical Robotics 2022-2023

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

Deadline for manuscript submissions: closed (23 June 2023) | Viewed by 24927

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Dr. Axel Krieger

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Guest Editor

Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
Interests: novel tools; image guidance; robot control techniques for medical robotics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tamás Haidegger

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Guest Editor

Antal Bejczy Center for Intelligent Robotics, Obuda University, Budapest, Hungary
Interests: surgical robotics; medical robot autonomy; robot safety and standardization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical/surgical robotics has a stunning 40-year history, and now, as innovations in the field rapidly accelerate, there has been an unprecedented rise in applications and systems. Surgical robotics is entering new domains, requiring highly sophisticated manipulation skills and decision making, while rehabilitation and assistive robotics are now conquering several domains. The newest generation of medical robots not only functions as an agile extension of human eyes and hands, but also serve to provide a skillful and smart partner to humans. The goal of this SI is to more broadly engage the medical/surgical robotics community, present the latest developments, and define the roadmap for future enhancements to these platforms. Apart from intelligent sensor technologies, smart mechatronics and data science tools, and articles on technical developments targeting ergonomics and usability, reports striving to establish a safe and reliable environment for Medical Robots 4.0 should also be included. Automated medical treatments promise a better quality of life for many, where eminence in science, engineering and design should go hand-in-hand, addressing technical-, safety- and performance-related questions. Your submissions are warmly welcomed.

Dr. Axel Krieger
Dr. Tamás Haidegger
Guest Editors

Manuscript Submission Information

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Published Papers (10 papers)

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Research

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17 pages, 9552 KiB

Open AccessArticle

A Modular 3-Degrees-of-Freedom Force Sensor for Robot-Assisted Minimally Invasive Surgery Research

by Zonghe Chua and Allison M. Okamura

Sensors 2023, 23(11), 5230; https://doi.org/10.3390/s23115230 - 31 May 2023

Cited by 5 | Viewed by 1996

Abstract

Effective force modulation during tissue manipulation is important for ensuring safe, robot-assisted, minimally invasive surgery (RMIS). Strict requirements for in vivo applications have led to prior sensor designs that trade off ease of manufacture and integration against force measurement accuracy along the tool axis. Due to this trade-off, there are no commercial, off-the-shelf, 3-degrees-of-freedom (3DoF) force sensors for RMIS available to researchers. This makes it challenging to develop new approaches to indirect sensing and haptic feedback for bimanual telesurgical manipulation. We present a modular 3DoF force sensor that integrates easily with an existing RMIS tool. We achieve this by relaxing biocompatibility and sterilizability requirements and by using commercial load cells and common electromechanical fabrication techniques. The sensor has a range of ±5 N axially and ±3 N laterally with errors of below 0.15 N and maximum errors below 11% of the sensing range in all directions. During telemanipulation, a pair of jaw-mounted sensors achieved average errors below 0.15 N in all directions. It achieved an average grip force error of 0.156 N. The sensor is for bimanual haptic feedback and robotic force control in delicate tissue telemanipulation. As an open-source design, the sensors can be adapted to suit other non-RMIS robotic applications. Full article

(This article belongs to the Special Issue Medical Robotics 2022-2023)

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21 pages, 17620 KiB

Open AccessArticle

Vibro-Acoustic Sensing of Instrument Interactions as a Potential Source of Texture-Related Information in Robotic Palpation

by Thomas Sühn, Nazila Esmaeili, Sandeep Y. Mattepu, Moritz Spiller, Axel Boese, Robin Urrutia, Victor Poblete, Christian Hansen, Christoph H. Lohmann, Alfredo Illanes and Michael Friebe

Sensors 2023, 23(6), 3141; https://doi.org/10.3390/s23063141 - 15 Mar 2023

Cited by 5 | Viewed by 2518

Abstract

The direct tactile assessment of surface textures during palpation is an essential component of open surgery that is impeded in minimally invasive and robot-assisted surgery. When indirectly palpating with a surgical instrument, the structural vibrations from this interaction contain tactile information that can [...] Read more.

α

and velocity

\vec{v}

on the vibro-acoustic signals from this indirect palpation. A 7-DOF robotic arm, a standard surgical instrument, and a vibration measurement system were used to palpate three different materials with varying

α

and

\vec{v}

. The signals were processed based on continuous wavelet transformation. They showed material-specific signatures in the time–frequency domain that retained their general characteristic for varying

α

and

\vec{v}

. Energy-related and statistical features were extracted, and supervised classification was performed, where the testing data comprised only signals acquired with different palpation parameters than for training data. The classifiers support vector machine and k-nearest neighbours provided

99.67 %

and

96.00 %

accuracy for the differentiation of the materials. The results indicate the robustness of the features against variations in the palpation parameters. This is a prerequisite for an application in minimally invasive surgery but needs to be confirmed in realistic experiments with biological tissues. Full article

(This article belongs to the Special Issue Medical Robotics 2022-2023)

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22 pages, 3361 KiB

Open AccessArticle

Inducing Performance of Commercial Surgical Robots in Space

by Timothy Sands

Sensors 2023, 23(3), 1510; https://doi.org/10.3390/s23031510 - 29 Jan 2023

Cited by 1 | Viewed by 2078

Abstract

Pre-existing surgical robotic systems are sold with electronics (sensors and controllers) that can prove difficult to retroactively improve when newly developed methods are proposed. Improvements must be somehow “imposed” upon the original robotic systems. What options are available for imposing performance from pre-existing, common systems and how do the options compare? Optimization often assumes idealized systems leading to open-loop results (lacking feedback from sensors), and this manuscript investigates utility of prefiltering, such other modern methods applied to non-idealized systems, including fusion of noisy sensors and so-called “fictional forces” associated with measurement of displacements in rotating reference frames. A dozen modern approaches are compared as the main contribution of this work. Four methods are idealized cases establishing a valid theoretical comparative benchmark. Subsequently, eight modern methods are compared against the theoretical benchmark and against the pre-existing robotic systems. The two best performing methods included one modern application of a classical approach (velocity control) and one modern approach derived using Pontryagin’s methods of systems theory, including Hamiltonian minimization, adjoint equations, and terminal transversality of the endpoint Lagrangian. The key novelty presented is the best performing method called prefiltered open-loop optimal + transport decoupling, achieving 1–3 percent attitude tracking performance of the robotic instrument with a two percent reduced computational burden and without increased costs (effort). Full article

(This article belongs to the Special Issue Medical Robotics 2022-2023)

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24 pages, 15464 KiB

Open AccessArticle

Design and Validation of a Low-Level Controller for Hierarchically Controlled Exoskeletons

by Connor W. Herron, Zachary J. Fuge, Madeline Kogelis, Nicholas J. Tremaroli, Bhaben Kalita and Alexander Leonessa

Sensors 2023, 23(2), 1014; https://doi.org/10.3390/s23021014 - 16 Jan 2023

Cited by 6 | Viewed by 2708

Abstract

In this work, a generalized low-level controller is presented for sensor collection, motor input, and networking with a high-level controller. In hierarchically controlled exoskeletal systems, which utilize series elastic actuators (SEAs), the hardware for sensor collection and motor command is separated from the computationally expensive high-level controller algorithm. The low-level controller is a hardware device that must collect sensor feedback, condition and filter the measurements, send actuator inputs, and network with the high-level controller at a real-time rate. This research outlines the hardware of two printed circuit board (PCB) designs for collecting and conditioning sensor feedback from two SEA subsystems and an inertial measurement unit (IMU). The SEAs have a joint and motor encoder, motor current, and force sensor feedback that can be measured using the proposed generalized low-level controller presented in this work. In addition, the high and low-level networking approach is discussed in detail, with a full breakdown of the data storage within a communication frame during the run-time operation. The challenges of device synchronization and updates rates of high and low-level controllers are also discussed. Further, the low-level controller was validated using a pendulum test bed, complete with full sensor feedback, including IMU results for two open-loop scenarios. Moreover, this work can be extended to other hierarchically controlled robotic systems that utilize SEA subsystems, such as humanoid robots, assistive rehabilitation robots, training simulators, and robotic-assisted surgical devices. The hardware and software designs presented in this work are available open source to enable researchers with a direct solution for data acquisition and the control of low-level devices in a robotic system. Full article

(This article belongs to the Special Issue Medical Robotics 2022-2023)

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19 pages, 4711 KiB

Open AccessArticle

Neural Network-Based Active Load-Sensing Scheme and Stiffness Adjustment for Pneumatic Soft Actuators for Minimally Invasive Surgery Support

by Yuxi Lu, Zhongchao Zhou, Shota Kokubu, Ruian Qin, Pablo E. Tortós Vinocour and Wenwei Yu

Sensors 2023, 23(2), 833; https://doi.org/10.3390/s23020833 - 11 Jan 2023

Cited by 1 | Viewed by 1932

Abstract

To provide a stable surgical view in Minimally Invasive Surgery (MIS), it is necessary for a flexible endoscope applied in MIS to have adjustable stiffness to resist different external loads from surrounding organs and tissues. Pneumatic soft actuators are expected to fulfill this role, since they could feed the endoscope with an internal access channel and adjust their stiffness via an antagonistic mechanism. For that purpose, it is essential to estimate the external load. In this study, we proposed a neural network (NN)-based active load-sensing scheme and stiffness adjustment for a soft actuator for MIS support with antagonistic chambers for three degrees of freedom (DoFs) of control. To deal with the influence of the nonlinearity of the soft actuating system and uncertainty of the interaction between the soft actuator and its environment, an environment exploration strategy was studied for improving the robustness of sensing. Moreover, a NN-based inverse dynamics model for controlling the stiffness of the soft actuator with different flexible endoscopes was proposed too. The results showed that the exploration strategy with different sequence lengths improved the estimation accuracy of external loads in different conditions. The proposed method for external load exploration and inverse dynamics model could be used for in-depth studies of stiffness control of soft actuators for MIS support. Full article

(This article belongs to the Special Issue Medical Robotics 2022-2023)

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11 pages, 3950 KiB

Open AccessArticle

Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human Interaction

by Fábio Cunha, Tiago Ribeiro, Gil Lopes and A. Fernando Ribeiro

Sensors 2023, 23(2), 825; https://doi.org/10.3390/s23020825 - 11 Jan 2023

Cited by 1 | Viewed by 2138

Abstract

In this work, a large-scale tactile detection system is proposed, whose development is based on a soft structure using Machine Learning and Computer Vision algorithms to map the surface of a forearm sleeve. The current application has a cylindrical design, whose dimensions intend to be like a human forearm or bicep. The model was developed assuming that deformations occur only at one section at a time. The goal for this system is to be coupled with the CHARMIE robot, a collaborative robot for domestic and medical environments. This system allows the contact detection of the entire forearm surface enabling interaction between a Human Being and a robot. A matrix with sections can be configured to present certain functionalities, allowing CHARMIE to detect contact in a particular section, and thus perform a specific behaviour. After building the dataset, an Artificial Neural Network (ANN) was created. This network was called Section Detection Network (SDN), and through Supervised Learning, a model was created to predict the contact location. Furthermore, Stratified K-Fold Cross Validation (SKFCV) was used to divide the dataset. All these steps resulted in Neural Network with a test data accuracy higher than 80%. Regarding the real-time evaluation, a graphical interface was structured to demonstrate the predicted class and the corresponding probability. This research concluded that the method described has enormous potential to be used as a tool for service robots allowing enhanced human-robot interaction. Full article

(This article belongs to the Special Issue Medical Robotics 2022-2023)

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18 pages, 27295 KiB

Open AccessArticle

Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System

by Arezki Abderrahim Chellal, José Lima, José Gonçalves, Florbela P. Fernandes, Fátima Pacheco, Fernando Monteiro, Thadeu Brito and Salviano Soares

Sensors 2022, 22(23), 9532; https://doi.org/10.3390/s22239532 - 6 Dec 2022

Cited by 5 | Viewed by 2556

Abstract

Rehabilitation robotics aims to facilitate the rehabilitation procedure for patients and physical therapists. This field has a relatively long history dating back to the 1990s; however, their implementation and the standardisation of their application in the medical field does not follow the same pace, mainly due to their complexity of reproduction and the need for their approval by the authorities. This paper aims to describe architecture that can be applied to industrial robots and promote their application in healthcare ecosystems. The control of the robotic arm is performed using the software called SmartHealth, offering a 2 Degree of Autonomy (DOA). Data are gathered through electromyography (EMG) and force sensors at a frequency of 45 Hz. It also proves the capabilities of such small robots in performing such medical procedures. Four exercises focused on shoulder rehabilitation (passive, restricted active-assisted, free active-assisted and Activities of Daily Living (ADL)) were carried out and confirmed the viability of the proposed architecture and the potential of small robots (i.e., the UR3) in rehabilitation procedure accomplishment. This robot can perform the majority of the default exercises in addition to ADLs but, nevertheless, their limits were also uncovered, mainly due to their limited Range of Motion (ROM) and cost. Full article

(This article belongs to the Special Issue Medical Robotics 2022-2023)

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18 pages, 700 KiB

Open AccessArticle

Scenario-Based Programming of Voice-Controlled Medical Robotic Systems

by Adam Rogowski

Sensors 2022, 22(23), 9520; https://doi.org/10.3390/s22239520 - 6 Dec 2022

Cited by 2 | Viewed by 2152

Abstract

An important issue in medical robotics is communication between physicians and robots. Speech-based communication is of particular advantage in robot-assisted surgery. It frees the surgeon’s hands; hence, he can focus on the principal tasks. Man-machine voice communication is the subject of research in various domains (industry, social robotics), but medical robots are very specific. They must precisely synchronize their activities with operators. Voice commands must be possibly short. They must be executed without significant delays. An important factor is the use of a vision system that provides visual information in direct synchronization with surgeon actions. Its functions could be also controlled using speech. The aim of the research presented in this paper was to develop a method facilitating creation of voice-controlled medical robotic systems, fulfilling the mentioned requirements and taking into account possible scenarios of man-machine collaboration in such systems. A robot skill description (RSD) format was proposed in order to facilitate programming of voice control applications. A sample application was developed, and experiments were conducted in order to draw conclusions regarding the usefulness of speech-based interfaces in medical robotics. The results show that a reasonable selection of system functions controlled by voice may lead to significant improvement of man-machine collaboration. Full article

(This article belongs to the Special Issue Medical Robotics 2022-2023)

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Review

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17 pages, 318 KiB

Open AccessReview

Application of Medical Image Navigation Technology in Minimally Invasive Puncture Robot

by Shuai Hu, Rongjian Lu, Yinlong Zhu, Wenhan Zhu, Hongzhe Jiang and Suzhao Bi

Sensors 2023, 23(16), 7196; https://doi.org/10.3390/s23167196 - 16 Aug 2023

Viewed by 1733

Abstract

Microneedle puncture is a standard minimally invasive treatment and surgical method, which is widely used in extracting blood, tissues, and their secretions for pathological examination, needle-puncture-directed drug therapy, local anaesthesia, microwave ablation needle therapy, radiotherapy, and other procedures. The use of robots for microneedle puncture has become a worldwide research hotspot, and medical imaging navigation technology plays an essential role in preoperative robotic puncture path planning, intraoperative assisted puncture, and surgical efficacy detection. This paper introduces medical imaging technology and minimally invasive puncture robots, reviews the current status of research on the application of medical imaging navigation technology in minimally invasive puncture robots, and points out its future development trends and challenges. Full article

(This article belongs to the Special Issue Medical Robotics 2022-2023)

15 pages, 1471 KiB

Open AccessEditor’s ChoiceReview

Robotic Technology in Foot and Ankle Surgery: A Comprehensive Review

by Taylor P. Stauffer, Billy I. Kim, Caitlin Grant, Samuel B. Adams and Albert T. Anastasio

Sensors 2023, 23(2), 686; https://doi.org/10.3390/s23020686 - 6 Jan 2023

Cited by 4 | Viewed by 3201

Abstract

Recent developments in robotic technologies in the field of orthopaedic surgery have largely been focused on higher volume arthroplasty procedures, with a paucity of attention paid to robotic potential for foot and ankle surgery. The aim of this paper is to summarize past and present developments foot and ankle robotics and describe outcomes associated with these interventions, with specific emphasis on the following topics: translational and preclinical utilization of robotics, deep learning and artificial intelligence modeling in foot and ankle, current applications for robotics in foot and ankle surgery, and therapeutic and orthotic-related utilizations of robotics related to the foot and ankle. Herein, we describe numerous recent robotic advancements across foot and ankle surgery, geared towards optimizing intra-operative performance, improving detection of foot and ankle pathology, understanding ankle kinematics, and rehabilitating post-surgically. Future research should work to incorporate robotics specifically into surgical procedures as other specialties within orthopaedics have done, and to further individualize machinery to patients, with the ultimate goal to improve perioperative and post-operative outcomes. Full article

(This article belongs to the Special Issue Medical Robotics 2022-2023)

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