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IEEE Transactions on Medical Robotics and Bionics
Number of Followers: 5  Hybrid journal (It can contain Open Access articles)ISSN (Online) 2576-3202 Published by IEEE  [228 journals]
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- IEEE Transactions on Medical Robotics and Bionics Publication Information
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Pages: C2 - C2
Abstract: null
PubDate: THU, 08 AUG 2024 09:17:24 -04
Issue No: Vol. 6, No. 3 (2024)
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- IEEE Transactions on Medical Robotics and Bionics Society Information
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Pages: C3 - C3
Abstract: null
PubDate: THU, 08 AUG 2024 09:17:24 -04
Issue No: Vol. 6, No. 3 (2024)
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- IEEE Transactions on Medical Robotics and Bionics Information for Authors
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Pages: C4 - C4
Abstract: null
PubDate: THU, 08 AUG 2024 09:17:24 -04
Issue No: Vol. 6, No. 3 (2024)
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- Guest Editorial Joining Efforts Moving Faster in Surgical Robotics
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Authors: Emmanuel Vander Poorten;Leonardo S. Mattos;Guillaume Morel;Paolo Fiorini;Alicia Casals;Arianna Menciassi;
Pages: 784 - 786
Abstract: The IEEE Transactions on Medical Robotics and Bionics (T-MRB) is an initiative shared by the two IEEE Societies of Robotics and Automation – RAS – and Engineering in Medicine and Biology – EMBS.
PubDate: THU, 08 AUG 2024 09:17:25 -04
Issue No: Vol. 6, No. 3 (2024)
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- Experimental Assessment of Positioning Precision During Free-Hand and
Robot-Assisted Tool Manipulation in Transoral Microsurgery Model-
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Authors: Sukrit Prasarnkleo;Jeroen Meulemans;Mouloud Ourak;Leonardo S. Mattos;Vincent Vander Poorten;Emmanuel Vander Poorten;
Pages: 787 - 795
Abstract: Transoral laser microsurgery (TLM) is a vocal cord cancer treatment where surgical tools reach the targeted region through the mouth. A robot-assisted system could aid in such operation yet there is limited understanding of the precision that is reachable at the level of the vocal folds. Therefore, this paper analyzed the baseline of human tool positioning capability during simulated TLM. In a simulated TLM environment, 31 participants navigated a probe to reach the target region of variable diameter ranging from 2.0 mm to 0.1 mm. The total execution time and the number of incorrect contacts were recorded. To assess the positioning potential under robotic assistance, 5 volunteers conducted the same tasks with the help of a co-manipulation robot. The minimum target diameter humans can precisely achieve at the vocal fold is 1.5 mm (time: mean ${=} \,\, 13$ .92 s, SD ${=} \,\, 12$ .30 s, incorrect contact: mean ${=} \,\, 2.71$ , SD ${=} \,\, 4.53$ ) while with the co-manipulation system, the precision can be improved to 0.2 mm (time: mean ${=} \,\, 21$ .20 s, SD ${=} \,\, 12$ .31 s, incorrect contact: mean ${=} \,\, 3.84$ , SD ${=} \,\, 2.95$ ). The experiments successfully established a baseline for free-hand precision reachable at the vocal fold and potential improvement through robot assistance.
PubDate: TUE, 02 JUL 2024 09:17:45 -04
Issue No: Vol. 6, No. 3 (2024)
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- Development of a Robotic Ultrasound System to Assist Ultrasound
Examination of Pregnant Women-
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Authors: Maria Bamaarouf;Flavien Paccot;Laurent Sarry;Hélène Chanal;
Pages: 796 - 805
Abstract: This research paper centers on addressing a common issue faced by clinicians during ultrasound examinations, namely work-related musculoskeletal disorders (WRMSDs). The implementation of robotic ultrasound has the potential to reduce these disorders using teleoperated assistance, collaborative support, or even autonomous systems. In this study, we introduce a new collaborative assisting system specifically designed for ultrasound examinations involving pregnant, obese patients. The primary objective is to devise a transparent co-manipulation strategy that enables clinicians to maintain their natural gestures during the procedure. The key principle behind this approach is to ensure that the robot functions as a helpful tool without interfering with the examination process. To achieve this, a novel co-manipulation control strategy is developed, which involves the computation of a virtual solid’s path based on the operator’s interaction. This approach presents numerous benefits in comparison to conventional control techniques. It demonstrates improvements in terms of accuracy, diminishes task execution time, facilitates a more intuitive parameter adjustment process, and necessitates less exertion of force by the operator. Consequently, it could potentially serve as a viable solution for addressing the challenges faced by sonographers. Hence, the potential benefit of this new co-manipulation method is demonstrated experimentally by comparison with impedance and active compliance control strategies.
PubDate: WED, 10 APR 2024 09:18:55 -04
Issue No: Vol. 6, No. 3 (2024)
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- Robotic Ultrasound-Guided Instrument Localization in Fetoscopy
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Authors: Daniel Costa;Gianni Borghesan;Mouloud Ourak;António Pedro Aguiar;Yuyu Cai;Emmanuel Vander Poorten;
Pages: 806 - 817
Abstract: Fetoscopic Endoluminal Tracheal Occlusion (FETO) is a minimally invasive fetal surgery (MIFS) aimed at mitigating the effects of Congenital Diaphragmatic Hernia (CDH). During FETO, a latex balloon is introduced in the fetal trachea using a fetoscope. Typically, this surgery is performed under ultrasound guidance which is provided by a sonographer who manually operates the ultrasound probe. This manual operation imposes a considerable physical and cognitive demand, placing a burden on the sonographer. This paper proposes a robotic ultrasound-based instrument tracking system that automates the probe position control while ensuring continuous visibility of the fetoscope in ultrasound images. The development of the proposed system is achieved with the completion of two tasks. Firstly, a series of fetoscope localization algorithms are developed and compared. Secondly, a task-based control for a robotic ultrasound system is developed. The localization algorithms’ performance is evaluated on annotated ultrasound datasets. The OEU-Net algorithm is selected based on this evaluation and is implemented in the instrument tracking system. The performance assessment of the tracking system shows that it is capable of tracking the fetoscope with a mean error below 4 mm. Thus, the developed system represents a significant advancement toward automatic robotic assistance for ultrasound guidance during FETO.
PubDate: THU, 30 MAY 2024 09:16:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Ultrasound-Based Robot-Assisted Drilling for Minimally Invasive Pedicle
Screw Placement-
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Authors: Ruixuan Li;Ayoob Davoodi;Maikel Timmermans;Kaat Van Assche;Orçun Taylan;Lennart Scheys;Matthias Tummers;Gianni Borghesan;Emmanuel Vander Poorten;
Pages: 818 - 828
Abstract: Minimally invasive pedicle screw placement (MIPSP) is a widely used treatment for spine diseases. When coupled with intraoperative navigation modalities, robots may help improve surgical outcomes and reduce complications. With such a system, the application of pedicle screws has been expanded from needle insertion to the spine surgery. This paper investigates the possibility and feasibility of robot-assisted MIPSP based on ultrasound (US) guidance. The proposed system is non-radiative and fiducial-free, using purely image information to close the registration loop. Then the system automatically positions the drill tip to a planned screw trajectory and executes the drilling operation. Experiments were conducted on both ex-vivo lamb and human cadaver spines. An entry point accuracy of $2.39\pm 1.41$ mm, and orientation accuracy of $2.82\pm 1.85^{\circ }$ was found for 24 drilled trajectories on three lamb spines. On the ex-vivo human spine, the position error averaged $3.08\pm 2.43$ mm at the entry point and $4.05\pm 2.62$ mm at the stop point across 16 drilling instances. Moreover, a $87.5\%$ success rate was reported by using Gertzbein-Robbins grade. The experimental results demonstrate the potential for offering a radiation-free alternative. Although restricted to cadaver trials, this work encourages further exploration of this technology to assist surgeons in maximizing performance in clinical practice.
PubDate: MON, 08 APR 2024 09:17:41 -04
Issue No: Vol. 6, No. 3 (2024)
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- A Multi-Sensorization Approach to Improve Safety in Transesophageal
Echocardiography-
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Authors: Giovanni Faoro;Izadyar Tamadon;Selene Tognarelli;Arianna Menciassi;
Pages: 829 - 838
Abstract: Real-time 3D transesophageal echocardiography (RT-3D TEE) allows 3D visualization of patient heart and catheters without exposing patient and operators to ionizing radiations. Nonetheless, during such procedures esophageal injuries occur due to improper probe manipulation and probe overheating. To tackle these problems, we propose a multisensorization approach to provide information on probe pose and temperature throughout the procedure. Electromagnetic (EM) tracking is fused with inertial sensing thanks to a finite state machine integrating Extended and Incremental Kalman filters. This approach allows for a statistically significant improvement in static tracking with respect to standard EM, as reported by the Mann-Withney test. A novel sensor fault detection based on angular velocities discrepancy allows for robust tracking under different electromagnetic interferences, such as the one provided by ferro-, dia- and paramagnetic materials occupying the interventional room. Fiber optic technology is exploited for temperature estimation, taking advantage of its immunity to EM fields and the possibility of distributed sensing. Performances are compared with a commercial thermistor to guarantee feasibility and a root mean square error of $1.59~^{\circ }$ C is finally reported. We believe that these results demonstrate how sensing technologies can be integrated in TEE-guided surgical procedures to improve overall outcome and safety.
PubDate: THU, 30 MAY 2024 09:16:24 -04
Issue No: Vol. 6, No. 3 (2024)
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- A Semi-Autonomous Control Mode for Flexible Steerable Intraluminal
Platforms-
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Authors: Fernando Gonzalez-Herrera;Florent Nageotte;Philippe Zanne;Gianni Borghesan;Michel de Mathelin;Emmanuel Vander Poorten;Benoit Rosa;
Pages: 839 - 850
Abstract: Flexible steerable intraluminal robot platforms allow treatment and screening of colorectal cancer at an early stage, potentially reducing the associated incidence and mortality rates. Such robotic platforms often rely on a tree-like flexible architecture, with a flexible robotized body carrying both the endoscope camera and two robotized flexible surgical arms at its distal end. Telemanipulating these robotic platforms to correctly perform surgical tasks is technically difficult due to their kinematic complexity and the demanding nature of the task, which leads to potential interruptions in the surgical workflow. In this paper, a technique to efficiently control the arms and body and correctly perform complex surgical steps during the endoscopic submucosal dissection procedure is proposed. The technique, referred to as semi-autonomous arm-body control, is based on a quadratic programming controller. Custom-defined tasks synergistically control the arms and body, while avoiding unsafe positions for the arms. Experiments in a mixed physical-simulated setup with eight users show an increased performance on the task and smoother movements compared to manual telemanipulation, at the expense of a slightly longer operating time. Further study will look at validating the approach in more realistic scenarios.
PubDate: MON, 08 APR 2024 09:17:41 -04
Issue No: Vol. 6, No. 3 (2024)
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- Robotics Application in Dentistry: A Review
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Authors: Zeyang Xia;Faizan Ahmad;Hao Deng;Lin Jiang;Wenlong Qin;Qunfei Zhao;Jing Xiong;
Pages: 851 - 867
Abstract: Digital dentistry and afterwards intelligent dentistry have been considered a trend in the development of both dental research and clinical practice. Robotics enhances precision and efficiency in medicine. In particular, robotics in dentistry is revolutionizing patient care with advanced technological integration, minimally invasive procedures, and improved outcomes and patient experiences. This review presents an in-depth concept of robots in digital dentistry, highlighting major contributions and impact in clinical scenarios. We first present the motivation behind dental robots and then will discuss the limitations and gaps between the research and applications of dental robots in different fields of dentistry. These robots are clinically involved in oral and maxillofacial surgery, dental implants, prosthodontics, orthognathic surgery, endodontics, and dental education treatments. The literature suggest that these robots are efficient, making quick decision, and maximize the benefit of digital dentistry. It fully automate the surgical procedure for diagnostic and treatment system. By integrating Artificial Intelligence (AI) to these robots eliminates the clinical decision making approach for predictive analysis for early detection and prevention. Finally, the key technologies and potential developments in robotics across various fields of dentistry were demonstrated. It is also discussed carefully how aspects such as mechanical design, recognition sensors, manipulation planning, and state monitoring can significantly influence the future impact of dental robots. These components play a crucial role in enhancing the functionality and efficiency of dental robotics, paving the way for advanced dental care. This review paper will enable researchers to gain better understanding of current status, challenges and future directions of dental robots.
PubDate: MON, 03 JUN 2024 09:17:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Development of Force Sensing Techniques for Robot-Assisted Laparoscopic
Surgery: A Review-
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Authors: Yupeng Hao;Han Zhang;Zhiqiang Zhang;Chengzhi Hu;Chaoyang Shi;
Pages: 868 - 887
Abstract: Robot-assisted laparoscopic surgery (RALS) has been widely investigated and developed as a routine and preferred minimally invasive surgery (MIS) because of enhanced operational precision and dexterity, improved visualization, and reduced surgeon stress and fatigue. However, the lack of force feedback poses challenges to accurate interaction force perception, lowered surgical errors, improved patient safety, and upgraded surgical outcomes. The solutions to force sensing can empower surgeons with a more intuitive and natural surgical experience with accurate perception capacity of interaction forces, efficient motor skill acquisition, enhanced surgical quality, and support the development of high-level techniques for surgical intelligence and autonomy. Although extensive research has been investigated in this field, effective and solid solutions are still unavailable for actual surgical scenarios. This review provides a comprehensive investigation from starting implementations to recent advances in emerging techniques for physical force sensors in laparoscopic surgery and RALS and focuses on the following categories: strain gauge-based, capacitive-based, and optical fiber-based principles. The design of force-sensitive structures from the mechanism perspective has been emphasized to provide possible and valuable design guidance for force sensor implementations with expected performance. Merits and limitations of existing technologies and prospects of new technologies are also discussed.
PubDate: THU, 30 MAY 2024 09:16:24 -04
Issue No: Vol. 6, No. 3 (2024)
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- Technologies for the Automation of Anatomic Pathology Processes: A Review
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Authors: Sabrina Ciancia;Lorenzo Vannozzi;Aliria Poliziani;Lorena Guachi-Guachi;Denise Amram;Dario Lunni;Alessandra Zucca;Marco Bellini;Luigi Spagnoli;Gian Andrea Pedrazzini;Andrea Cavazzana;Leonardo Ricotti;
Pages: 888 - 902
Abstract: One of the primary roles of an anatomic pathology laboratory (APL) is the identification of tissue abnormalities, which is crucial for diagnosing diseases and defining a suitable therapy. To date, a considerable number of human errors and artifacts affect the APL test cycle in all its phases (pre-analytical, intra-analytical, analytical, and post-analytical), mainly due to manual and non-standardized procedures. An extensive use of technologies (among which robotic ones) aimed at favoring laboratory automation procedures, would be key in decreasing these errors and their clinical consequences. However, several improvements in workflow, technology and standardization still need to occur. In this review, we discuss the current level of automation currently available in the APL histopathologic production workflow in all phases of the test cycle, highlighting the legal and ethical issues related to their adoption.
PubDate: MON, 01 JUL 2024 09:16:54 -04
Issue No: Vol. 6, No. 3 (2024)
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- Advancements in Soft Wearable Robots: A Systematic Review of Actuation
Mechanisms and Physical Interfaces-
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Authors: Sajjad Hussain;Fanny Ficuciello;
Pages: 903 - 929
Abstract: Soft actuators and robotic devices designed for rehabilitation and assistance are a rapidly growing field of research. Their inherent flexibility enhances comfort and usability without restricting the user’s natural range of motion. However, despite these advantages, there are still several challenges that need to be addressed before these systems can be commercialized. This paper presents a comprehensive review of the latest developments in soft wearable robots, also known as exosuits. Soft exosuits are composed of two main components: actuation mechanisms (how forces/torques are generated) and physical interfaces (how and where the robot is anchored to the body). This paper reviews the advances in these two areas, while categorizing exosuits based on the intended assisted joint, assisted degrees of freedom (DOF), and device type. The systematic literature review follows the PRISMA guidelines to summarize the relevant studies and investigate their related physical interface, actuation mechanism and its design. Several limitations were identified in these areas, and insights into potential future research directions are presented. In the future, the goal should be to develop an untethered assistive device that can provide assistance to multiple joints while having a low form factor, an intuitive and natural interface, and being comfortable for the user.
PubDate: THU, 30 MAY 2024 09:16:24 -04
Issue No: Vol. 6, No. 3 (2024)
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- A Review of Proprioceptive Feedback Strategies for Upper-Limb Myoelectric
Prostheses-
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Authors: Olivier Lecompte;Sofiane Achiche;Abolfazl Mohebbi;
Pages: 930 - 939
Abstract: Upper extremity prostheses have seen significant technological advances in recent years, primarily with the advent of myoelectric prostheses and other designs incorporating mechatronic elements. Although they do not replicate the functionality of the natural hand, users now have a way of communicating their movement intentions to the prosthesis. However, the lack of physiological feedback from the device to the user can hinder proper integration of the prosthesis, and can be a contributing factor in the rejection of the technology. This is why experts point out that sensory feedback is one of the main missing features of commercial prostheses. The literature surrounding the restoration of somatosensation primarily discusses strategies to emulate tactile perception, but few address proprioceptive perception, which is the ability to perceive limb position and movement. Yet, proprioception has been shown to be a crucial element in object manipulation. This article offers an in-depth look into the literature surrounding proprioceptive perception restoration strategies for users of upper limb prostheses by identifying and comparing the documented strategies in relation to the concept of an optimal sensory feedback restoration device.
PubDate: THU, 30 MAY 2024 09:16:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Design and Evaluation for a Soft Intra-Abdominal Wireless Laparoscope
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Authors: Hui Liu;Ning Li;Shuai Li;Gregory J. Mancini;Jindong Tan;
Pages: 940 - 950
Abstract: In single-incision laparoscopic surgery (SILS), magnetic anchoring and guidance system (MAGS) is a promising technique to prevent clutter in the surgical workspace and provide a larger vision field. Existing camera designs mainly rely on a rigid structure and sliding motion, which may cause stress concentration and tissue damage on curved abdominal walls. Meanwhile, the insertion procedure is also challenging. In this paper, we proposed a wireless MAGS consisting of soft material and wheel structure design. The camera can passively bend and adapt to the curved tissue surface to relieve stress concentration. The wheel structure transfers the sliding motion to rolling motion when the camera tilts and translates, avoiding tissue rupture due to dry friction and facilitating smooth motion. The experiments show the novel laparoscope has dexterous locomotion and bendability with 20° in bending angle and $16.4mm$ in displacement. The maximum stress is reduced by 64% compared with rigid designs. An easy and safe insertion procedure based on soft property is also introduced, which takes less than 2 minutes on average without the assistance of additional instruments.
PubDate: THU, 18 APR 2024 09:16:37 -04
Issue No: Vol. 6, No. 3 (2024)
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- A Pneumatic Driven MRI-Guided Robot System for Prostate Interventions
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Authors: Haipeng Liang;Wanli Zuo;Dimitri Kessler;Tristan Barrett;Zion Tsz Ho Tse;
Pages: 951 - 960
Abstract: Under the guidance of high-resolution Magnetic Resonance Imaging (MRI), robotic devices offer a great advantage for prostate intervention. This paper presents an MR-safe robot, where a needle is attached to the needle guide to obtain prostate biopsies during surgeries. The robot is powered by three actuators, two of them are customized to function as a work plane that allows the needle to move horizontally and vertically, and the third actuator controls the rotation of the work plane, allowing the needle to be inserted into the prostate from different directions. All the actuators are pneumatically actuated to allow them to work in a Magnetic Resonance (MR) environment. The kinematics and mechanism of the robot are analyzed. A user interface developed using LabView is created to calculate the target position and generate a control signal for the valves. In the open-air test, the needle can reach the target with an accuracy of 1.3 mm. The signal-to-noise ratio (SNR) variation was measured below 5% under a 3T MR scanner.
PubDate: TUE, 16 APR 2024 09:16:13 -04
Issue No: Vol. 6, No. 3 (2024)
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- Pose-Independent Interaction Distance Adjustment for Magnetically Driven
Robotic Capsules-
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Authors: Guoqing Li;Jing Li;Gastone Ciuti;Paolo Dario;Qiang Huang;
Pages: 961 - 970
Abstract: Safe capsule-colon interaction for magnetically driven robotic capsules is important in clinical applications. This work presents a solution based on the amplitude information of the magnetic field to adjust the distance between the interacting magnets, in order to prevent the magnetic forces exerted on the capsule robot and the pressure on the intestine walls from being overlarge, which may cause large deformation of the colon. As the first step, the geometry of the internal magnet embedded in the capsule is optimized to approach a near-spherical amplitude of the magnetic field based on the dipole model. Next, mathematical mapping from magnetic field amplitude to the interaction distance between the magnets is presented with constraint derivation and implementation. Then, a strategy to adjust the distance between the interacting magnets is provided based on the mapping using the magnetic field information. Finally, experiments are designed to validate the pose-independent interaction distance adjustment. Compared with the previous work, the proposed solution enables the quick interaction distance adjustment between the magnets to enhance the safety of capsule-colon interaction in the magnetically driven capsule endoscopies, since the interaction distance is derived straightforwardly from the magnetic field signals, without requiring the prerequisite implementation of capsule localization.
PubDate: MON, 03 JUN 2024 09:17:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- CathSim: An Open-Source Simulator for Endovascular Intervention
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Authors: Tudor Jianu;Baoru Huang;Minh Nhat Vu;Mohamed E. M. K. Abdelaziz;Sebastiano Fichera;Chun-Yi Lee;Pierre Berthet-Rayne;Ferdinando Rodriguez y Baena;Anh Nguyen;
Pages: 971 - 979
Abstract: Autonomous robots in endovascular operations have the potential to navigate circulatory systems safely and reliably while decreasing the susceptibility to human errors. However, there are numerous challenges involved with the process of training such robots, such as long training duration and safety issues arising from the interaction between the catheter and the aorta. Recently, endovascular simulators have been employed for medical training but generally do not conform to autonomous catheterization due to the lack of standardization and RL framework compliance. Furthermore, most current simulators are closed-source, which hinders the collaborative development of safe and reliable autonomous systems through shared learning and community-driven enhancements. In this work, we introduce CathSim, an open-source simulation environment that accelerates the development of machine learning algorithms for autonomous endovascular navigation. We first simulate the high-fidelity catheter and aorta with a state-of-the-art endovascular robot. We then provide the capability of real-time force sensing between the catheter and the aorta in simulation. Furthermore, we validate our simulator by conducting two different catheterization tasks using two popular reinforcement learning algorithms, namely SAC and PPO. The experimental results show that our open-source simulator can mimic the behavior of real-world endovascular robots and facilitate the development of different autonomous catheterization tasks. Our simulator is publicly available at https://github.com/airvlab/cathsim.
PubDate: FRI, 05 JUL 2024 09:16:12 -04
Issue No: Vol. 6, No. 3 (2024)
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- Electromagnets Under the Table: An Unobtrusive Magnetic Navigation System
for Microsurgery-
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Authors: Adam Schonewille;Changyan He;Cameron Forbrigger;Nancy Wu;James Drake;Thomas Looi;Eric Diller;
Pages: 980 - 991
Abstract: Miniature magnetic tools have the potential to enable minimally invasive surgical techniques to be applied to space-restricted surgical procedures in areas such as neurosurgery. However, typical magnetic navigation systems, which create the magnetic fields to drive such tools, either cannot generate large enough fields, or surround the patient in a way that obstructs surgeon access to the patient. This paper introduces the design of a magnetic navigation system with eight electromagnets arranged completely under the operating table, to endow the system with maximal workspace accessibility, which allows the patient to lie down on the top surface of the system without any constraints. The found geometric layout of the electromagnets maximizes the field strength and uniformity over a reasonable neurosurgical operating volume. The system can generate non-uniform magnetic fields up to 38 mT along the x and y axes and 47 mT along the z axis at a working distance of 120 mm away from the actuation system workbench, deep enough to deploy magnetic microsurgical tools in the brain. The forces which can be exerted on millimeter-scale magnets used in prototype neurosurgical tools are validated experimentally. Due to its large workspace, this system could be used to control milli-robots in a variety of surgical applications.
PubDate: MON, 01 JUL 2024 09:16:53 -04
Issue No: Vol. 6, No. 3 (2024)
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- Low-Profile 6-Axis Differential Magnetic Force/Torque Sensing
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Authors: David G. Black;Amir Hossein Hadi Hosseinabadi;Nicholas Rangga Pradnyawira;Mika Nogami;Septimu E. Salcudean;
Pages: 992 - 1003
Abstract: Force/torque sensing on hand-held tools enables control of applied forces, which is often essential in both tele-robotics and remote guidance of people. However, existing force sensors are either bulky, complex, or have insufficient load rating. This paper presents a novel 6 axis force-torque sensor based on differential magnetic field readings in a collection of low-profile sensor modules placed around a tool or device. The instrumentation is easy to install but nonetheless achieves good performance. A detailed mathematical model and optimization-based design procedure are also introduced. The modeling, simulation, and optimization of the force sensor are described and then used in the electrical and mechanical design and integration of the sensor into an ultrasound probe. Through a neural network-based nonlinear calibration, the sensor achieves average root-mean-square test errors of 0.41 N and 0.027 Nm compared to an off-the-shelf ATI Nano25 sensor, which are 0.80% and 1.16% of the full-scale range respectively. The sensor has an average noise power spectral density of less than 0.0001 N/ $\sqrt {\text {Hz}}$ , and a 95% confidence interval resolution of 0.0086 N and 0.063 Nmm. The practical readout rate is 1.3 kHz over USB serial and it can also operate over Bluetooth or Wi-Fi. This sensor can enable instrumentation of manual tools to improve the performance and transparency of teleoperated or autonomous systems.
PubDate: THU, 30 MAY 2024 09:16:24 -04
Issue No: Vol. 6, No. 3 (2024)
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- Machine-Learning-Based Multi-Modal Force Estimation for Steerable Ablation
Catheters-
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Authors: Elaheh Arefinia;Jayender Jagadeesan;Rajni V. Patel;
Pages: 1004 - 1016
Abstract: Catheter-based cardiac ablation is a minimally invasive procedure for treating atrial fibrillation (AF). Electrophysiologists perform the procedure under image guidance during which the contact force between the heart tissue and the catheter tip determines the quality of lesions created. This paper describes a novel multi-modal contact force estimator based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The estimator takes the shape and optical flow of the deflectable distal section as two modalities since frames and motion between frames complement each other to capture the long context in the video frames of the catheter. The angle between the tissue and the catheter tip is considered a complement of the extracted shape. The data acquisition platform measures the two-degrees-of-freedom contact force and video data as the catheter motion is constrained in the imaging plane. The images are captured via a camera that simulates single-view fluoroscopy for experimental purposes. In this sensor-free procedure, the features of the images and optical flow modalities are extracted through transfer learning. Long Short-Term Memory Networks (LSTMs) with a memory fusion network (MFN) are implemented to consider time dependency and hysteresis due to friction. The architecture integrates spatial and temporal networks. Late fusion with the concatenation of LSTMs, transformer decoders, and Gated Recurrent Units (GRUs) are implemented to verify the feasibility of the proposed network-based approach and its superiority over single-modality networks. The resulting mean absolute error, which accounted for only 2.84% of the total magnitude, was obtained by collecting data under more realistic circumstances in contrast to previous research studies. The decrease in error is considerably better than that achieved by individual modalities and late fusion with concatenation. These results emphasize the practicality and relevance of utilizing a multi-modal network in real-world scenarios.
PubDate: FRI, 31 MAY 2024 09:16:35 -04
Issue No: Vol. 6, No. 3 (2024)
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- Triple-Supervised Convolutional Transformer Aggregation for Robust
Monocular Endoscopic Dense Depth Estimation-
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Authors: Wenkang Fan;Wenjing Jiang;Hong Shi;Hui-Qing Zeng;Yinran Chen;Xiongbiao Luo;
Pages: 1017 - 1029
Abstract: Accurate deeply learned dense depth prediction remains a challenge to monocular vision reconstruction. Compared to monocular depth estimation from natural images, endoscopic dense depth prediction is even more challenging. While it is difficult to annotate endoscopic video data for supervised learning, endoscopic video images certainly suffer from illumination variations (limited lighting source, limited field of viewing, and specular highlight), smooth and textureless surfaces in surgical complex fields. This work explores a new deep learning framework of triple-supervised convolutional transformer aggregation (TSCTA) for monocular endoscopic dense depth recovery without annotating any data. Specifically, TSCTA creates convolutional transformer aggregation networks with a new hybrid encoder that combines dense convolution and scalable transformers to parallel extract local texture features and global spatial-temporal features, while it builds a local and global aggregation decoder to effectively aggregate global features and local features from coarse to fine. Moreover, we develop a self-supervised learning framework with triple supervision, which integrates minimum photometric consistency and depth consistency with sparse depth self-supervision to train our model by unannotated data. We evaluated TSCTA on unannotated monocular endoscopic images collected from various surgical procedures, with the experimental results showing that our methods can achieve more accurate depth range, more complete depth distribution, more sufficient textures, better qualitative and quantitative assessment results than state-of-the-art deeply learned monocular dense depth estimation methods.
PubDate: FRI, 31 MAY 2024 09:16:35 -04
Issue No: Vol. 6, No. 3 (2024)
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- ENeRF-SLAM:#A Dense Endoscopic SLAM With Neural Implicit Representation
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Authors: Jiwei Shan;Yirui Li;Ting Xie;Hesheng Wang;
Pages: 1030 - 1041
Abstract: Quantitative calculations of camera poses and dense anatomical reconstructions from endoscopic videos are essential for applications such as intraoperative navigation and robotic surgery automation. Prior studies on this task either overlook the unique characteristics of endoscopic scenes or produce reconstructions with numerous gaps due to limited observations, significantly limiting their practical application. Inspired by recent advancements in neural rendering, we develop a dense visual SLAM system that employs neural implicit representations, specifically designed for endoscopic sequences. By incorporating 3D geometric scene priors, our system effectively predicts and fills in unseen areas, ensuring the continuous and complete reconstruction of the scene. Taking into account the dynamic nature of the light source and the confined anatomy of the human body, we propose a neural implicit representation method designed for endoscopic scenes. Additionally, we introduce a hybrid tracking method that merges Gauss-Newton and gradient-based pose optimization, improving geometric consistency across frames. This reduces reliance on precise data matching and significantly enhances camera tracking accuracy. Extensive experiments on both synthetic and real medical endoscopic datasets demonstrate that our method outperforms existing systems in terms of scene reconstruction quality, camera tracking accuracy, and image rendering quality. Code is available at: https://github.com/Mar-lll/ENeRF-SLAM
PubDate: THU, 30 MAY 2024 09:16:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- A Series-Parallel Hybrid Pelvic Fracture Reduction Surgical Robotic System
Based on Novel 6-DOF Force Amplification Mechanism-
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Authors: Qianxin Wang;Shaolin Lu;Pengyun Liu;Yuanyuan Yang;Qiong Wang;Bing Li;Shibo Cai;Lihai Zhang;Jianwei Zhang;Ying Hu;
Pages: 1042 - 1053
Abstract: Robot-assisted closed reduction surgery is recognized as the optimal approach for pelvic fractures. However, existing surgical robotic systems often lack the necessary output force to overcome soft tissue tension. To address this limitation, we propose a novel surgical robotic system to deliver high output force with required workspace. Our approach includes the design of a 6-DOF parallel mechanism that utilizes the lever principle for force amplification. The distribution laws of its effective workspace and force amplification gain coefficient are investigated. Additionally, a series-parallel hybrid surgical robot system is developed, which demonstrates the amplification effect on the output force through the force amplification mechanism. To ensure smooth operation of the system, a comprehensive surgical operation framework isdevised that encompasses target pose planning, reduction path planning, real-time intraoperative navigation and control. Modeling experiments show promising results, with an average reduction accuracy of 0.31 mm and 0.24° under no load, and 1.03 mm and 0.34° under loads ranging from 0N to 475N. These findings highlight the effectiveness of our proposed force amplification mechanism in achieving substantial output force amplification while alleviating the surgeon’s burden. Moreover, our robotic system demonstrates the capability to achieve precise pelvic fracture reduction, significantly enhancing surgical outcomes.
PubDate: THU, 30 MAY 2024 09:16:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Model-Based Offline Reinforcement Learning for Autonomous Delivery of
Guidewire-
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Authors: Hao Li;Xiao-Hu Zhou;Xiao-Liang Xie;Shi-Qi Liu;Zhen-Qiu Feng;Mei-Jiang Gui;Tian-Yu Xiang;De-Xing Huang;Zeng-Guang Hou;
Pages: 1054 - 1062
Abstract: Guidewire delivery is a fundamental procedure in percutaneous coronary intervention. The inherent flexibility of the guidewire poses challenges in precise control, necessitating long-term training and substantial expertise. In response, this paper proposes a novel offline reinforcement learning (RL) algorithm, Conservative Offline Reinforcement Learning with Variational Environment Model (CORVE), for autonomous delivery of guidewire. CORVE first uses offline data to train an environment model and then optimizes the policy with both offline and model-generated data. The proposed method shares an encoder between the environmental model, policy, and Q-function, mitigating the common sample inefficiency in image-based RL. Besides, CORVE utilizes model prediction errors to forecast wrong deliveries in inference, which is an attribute absent in existing methods. The experimental results show that CORVE obtains superior performance in guidewire deliveries, achieving notably higher success rates and smoother movements than existing methods. These findings suggest that CORVE holds significant potential for enhancing the autonomy of vascular robotic systems in clinical settings.
PubDate: THU, 30 MAY 2024 09:16:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Soft Robotic Gastroscope for Low/Middle-Income Countries: Design and
Preliminary Validation-
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Authors: Xuyang Ren;Yu Huan;Martina Finocchiaro;Matteo Cianchetti;Giacomo Lo Secco;Shuxin Wang;Paolo Dario;Anastasios Koulaouzidis;Alberto Arezzo;Gastone Ciuti;
Pages: 1063 - 1072
Abstract: To reduce incidence and mortality, screening of the gastric cavity is crucial to diagnose early-stage cancers. Most cases are concentrated in low/middle-income countries (LMICs), where medical resources are limited. In this paper, we propose a miniaturized, disposable, and low-cost soft robotic gastroscope designed for screening in LMICs. The robotic platform is composed of i) a frontal soft-core module, ii) a flexible multi-lumen tether, and iii) an intuitive control handle, to provide a) a 180 deg bending angle, b) a 360 deg axial rotation, and c) linear movements with a 15 mm fine adjustment. Thanks to a single internal bending chamber, the diameter of the soft-core module and the tether are reduced to 7.2 mm and 4.3 mm, respectively. Mechanical performance, operational functionalities, and clinical dependability were successfully evaluated through in-vitro and ex-vivo experiments. In summary, given i) low-cost (i.e., ~25 USD), ii) low invasiveness, iii) high portability, and iv) intuitive control, the disposable soft gastroscope might have considerable clinical potential for widening gastric cancer screening in LMICs.
PubDate: THU, 30 MAY 2024 09:16:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Simulation-Based Flexible Needle Control With Single-Core FBG Feedback for
Spinal Injections-
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Authors: Yanzhou Wang;Yangsheng Xu;Jiarong Kang;Jan Fritz;Iulian Iordachita;
Pages: 1073 - 1083
Abstract: Objective: We present a general framework of simultaneous needle shape reconstruction and control input generation for robot-assisted spinal injection procedures, without continuous imaging feedback. Methods: System input-output mapping is generated with a real-time needle-tissue interaction simulation, and single-core FBG sensor readings are used as local needle shape feedback within the same simulation framework. FBG wavelength shifts due to temperature variation is removed by exploiting redundancy in fiber arrangement. Results: Targeting experiments performed on both plastisol lumbar phantoms as well as an ex vivo porcine lumbar section achieved in-plane tip errors of $0.6 \pm 0.3$ mm and $1.6 \pm 0.9$ mm, and total tip errors of $0.9 \pm 0.7$ mm and $2.1 \pm 0.8$ mm for the two testing environments. Significance: Our clinically inspired control strategy and workflow is self-contained and not dependent on the modality of imaging guidance. The generalizability of the proposed approach can be applied to other needle-based interventions where medical imaging cannot be reliably utilized as part of a closed-loop control system for needle guidance.
PubDate: TUE, 02 JUL 2024 09:17:45 -04
Issue No: Vol. 6, No. 3 (2024)
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- Breach Detection in Spine Surgery Based on Cutting Torque
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Authors: E. Saghbiny;L. Leblanc;A. Harlé;C. Bobbio;R. Vialle;G. Morel;B. Tamadazte;
Pages: 1084 - 1092
Abstract: The accurate placement of pedicle screws is crucial for various spinal interventions, demanding precise geometric alignment while carrying inherent risks. Studies show that the rate of complications can reach up to 18% in case of imprecise placement of pedicle screws. To enhance the precision and safety of pedicle screw placement, we have developed a robotic system equipped with several sensors and paired with a breach detection algorithm capable of identifying potential breaches in the spinal canal. The breach detection algorithm was conceptualized through an analysis of the cutting torque of the drill system. An ex-vivo experiment was conducted to assess the effectiveness of the developed robotic solution and breach detection algorithm. The data (e.g., cutting torque, position, velocity, etc.) used during the validation were collected by drilling 80 pedicles in fresh porcine vertebrae. The results demonstrated that the proposed algorithm could predict breaches in 96.42% of cases, i.e., the distance between the detected point (drilling stop) and the point of the breach is within 2 mm. In a single instance, the detection occurred earlier than anticipated due to the trajectory being oriented significantly medially, resulting in an initial interaction with the cortical bone at an earlier point.
PubDate: THU, 11 JUL 2024 09:16:47 -04
Issue No: Vol. 6, No. 3 (2024)
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- Label-Free Adaptive Gaussian Sample Consensus Framework for Learning From
Perfect and Imperfect Demonstrations-
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Authors: Yi Hu;Zahra Samadikhoshkho;Jun Jin;Mahdi Tavakoli;
Pages: 1093 - 1103
Abstract: Autonomous robotic surgery represents one of the most groundbreaking advancements in medical technology. Learning from human demonstrations is promising in this domain, which facilitates the transfer of skills from humans to robots. However, the practical application of this method is challenged by the difficulty of acquiring high-quality demonstrations. Surgical tasks often involve complex manipulations and stringent precision requirements, leading to frequent errors in the demonstrations. These imperfect demonstrations adversely affect the performance of controller policies learned from the data. Unlike existing methods that rely on extensive human labeling of demonstrated trajectories, we present a novel label-free adaptive Gaussian sample consensus framework to progressively refine the control policy. We demonstrate the efficacy and practicality of our approach through two experimental studies: a handwriting classification task, providing reproducible ground-truth labels for evaluation, and an endoscopy scanning task, demonstrating the feasibility of our method in a real-world clinical context. Both experiments highlight our method’s capacity to efficiently adapt to and learn from an ongoing stream of imperfect demonstrations.
PubDate: WED, 03 JUL 2024 09:16:55 -04
Issue No: Vol. 6, No. 3 (2024)
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- A Digital Twin-Based Large-Area Robot Skin System for Safer Human-Centered
Healthcare Robots Toward Healthcare 4.0-
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Authors: Geng Yang;Zhiqiu Ye;Haiteng Wu;Chen Li;Ruohan Wang;Depeng Kong;Zeyang Hou;Huafen Wang;Xiaoyan Huang;Zhibo Pang;Na Dong;Gaoyang Pang;
Pages: 1104 - 1115
Abstract: The fourth revolution of healthcare technologies, i.e., Healthcare 4.0, is putting robotics into human-dominated environments. In such a context, one of the main challenges is to develop human-centered robotics technologies that enable safe and reliable human-robot interaction toward human-robot symbiosis. Herein, robot skin is developed to endow healthcare robots with on-body proximity perception so as to fulfill the promise of safe and reliable robotic systems alongside humans. The sensing performance of the robot skin is evaluated by extensive experiments, providing important guidance on its effective implementation into a specific robot platform. Results show that the developed robot skin has a detection range of 0–50 mm, a maximum sensitivity of 0.7 pF/mm, a minimum resolution of 0.05 mm, a repeatability error of 6.6%, a hysteresis error of 7.1%, and bending durability of 2000 cycles. The robot skin is further customized and scaled up to form a large-area sensing system on the exterior of robot arms to support functional safety, which is experimentally validated by approaching distance monitoring and reactive collision avoidance. During the validation, the sensing feedback of the robot skin and the motion of the host robot are visualized remotely in the robot digital twin in a real-time manner via a cloud server. The cloud-based monitoring interface bridges the gap between local healthcare robots and remote professionals, illustrating promising applications where professionals monitor the robot state and intervene in challenging situations to provide instant support for emergent safety issues in human-robot interaction.
PubDate: MON, 01 JUL 2024 09:16:53 -04
Issue No: Vol. 6, No. 3 (2024)
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- Toward Human-Out-of-the-Loop Endoscope Navigation Based on Context
Awareness for Enhanced Autonomy in Robotic Surgery-
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Authors: Ziyang Chen;Ke Fan;Laura Cruciani;Matteo Fontana;Lorenzo Muraglia;Francesco Ceci;Laura Travaini;Giancarlo Ferrigno;Elena De Momi;
Pages: 1116 - 1124
Abstract: Although the da Vinci surgical system enhances manipulation dexterity and restores 3D vision in robotic surgery, it requires surgeons to asynchronously control surgical instruments and the endoscope, which hinders a smooth operation. Surgeons frequently position the endoscope to maintain a good field of view during operation, potentially increasing surgical time and workload. In this paper, a Human-Out-Of-The-Loop (HOOTL) endoscope navigation control with the assistance of context awareness is proposed to enhance surgical autonomy. A comprehensive comparison study using 8 state-of-the-art networks was conducted to find out the best model for surgical phase recognition. Ten human subjects were invited to participate in a classic ring transferring task based on three different endoscope navigation pipelines on a da Vinci research kit platform, including standard endoscope navigation, semi-autonomous endoscope navigation with manual pedal control, and HOOTL endoscope navigation supported by vision-based phase recognition. The experimental results showed that the proposed endoscope navigation approach releases the operation need of controlling the pedals, and it significantly reduces the execution time compared to the other two navigation pipelines. The result of the NASA Task Load Index (NASA-TLX) questionnaire indicates that the proposed endoscope navigation can reduce the physical and mental load for the users.
PubDate: WED, 03 JUL 2024 09:16:55 -04
Issue No: Vol. 6, No. 3 (2024)
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- A Kinematically Informed Approach to Near-Future Joint Angle Estimation at
the Ankle-
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Authors: Ryan S. Pollard;David S. Hollinger;Iván E. Nail-Ulloa;Michael E. Zabala;
Pages: 1125 - 1134
Abstract: Elevated runtimes of machine learning algorithms and neural networks make their inclusion in near-future joint angle estimation difficult. The purpose of this study was to develop simple, analytical models that prioritize historical joint kinematics when estimating near-future joint angles. Five kinematically-informed and extrapolation-based methods were developed for joint angle estimation at three near-future estimation horizons: $t_{pred} = 50$ ms, 75 ms, and 100 ms. The estimation error and required runtimes of each prediction algorithm were evaluated on the sagittal-plane ankle angles of 24 individual subjects who performed three level-ground walking trials. Results showed that the kinematically-informed models had significantly faster estimation runtimes than Random Forest (RF) machine learning models trained and tested on identical datasets (kinematic models: $t_{run}\lt 0.62$ ms, RF models: $t_{run}\gt 8.19$ ms for all estimation horizons). The RF models exhibited significantly lower prediction errors than the kinematic models for estimation horizons of $t_{pred} = 75$ ms and 100 ms, but no significance was found between the top-performing kinematic model and RF models for a $t_{pred} = 50$ ms. These results indicate that a kinematically-informed approach to joint angle estimation can serve as a simple alternative to complex machine learning models for very near-future applications ( $t_{pred} \leq 50$ ms) while serving as a comparison baseline for more distant estimation horizons ( $t_{pred} \geq 75$ ms).
PubDate: MON, 03 JUN 2024 09:17:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Wearable Continuous Gait Phase Estimation During Walking, Running,
Turning, Stairs, and Over Uneven Terrain-
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Authors: Linghui Xu;Yuting Chen;Bingfei Fan;Canjun Yang;Wei Yang;
Pages: 1135 - 1146
Abstract: Wearable continuous gait phase estimation is essential for walking assistance, clinical rehabilitation, and clinical assessment; however, most algorithms have only been validated for straight-line and constant-speed walking, and it is unclear how performance will change in real-life locomotion scenarios. A generalized paradigm is needed to comprehensively assess and recommend wearable continuous gait phase estimation strategies for the diverse array of walking situations. We thus propose a comprehensive evaluation indicator system for eight typical gait activities in daily life including slow walking, standard walking, running, walking with turns, stair descent, stair ascent, stop-and-go, and uneven terrain walking.The indicator system was used to evaluate four commonly used continuous gait phase estimation strategies: adaptive oscillators, phase oscillator, neural network, and time-based estimation. Eleven healthy participants were enrolled in the evaluation. All estimation strategies performed well for constant-speed walking but performance varied for other activities. Time-based estimation was most accurate for slowwalking ( $0.094~\pm ~0.011$ rad root mean square error, $1.50~\pm ~0.18$ % of one gait cycle), running ( $0.167~\pm ~0.028$ rad, $2.66~\pm ~0.44$ %) and walking with turns ( $0.124~\pm ~0.047$ rad, $2.00~\pm ~0.75$ %). Adaptive oscillators were most accurate for standard walking( $0.115~\pm ~0.037$ rad, $1.83~\pm ~0.59$ %). Phase oscillator was most accurate for stair climbing( $0.280~\pm ~0.063$ rad, $4.46~\pm ~1.00$ %) and uneven terrain ( $0.204~\pm ~0.069$ rad, $4.30~\pm ~1.10$ %). Neural network was most accurate for stop-and-go( $0.27~\pm ~0.114$ rad, $4.30~\pm ~1.81$ %). These results can potentially provide guidance for determining suitable gait phase estimation strategies in realistic locomotion scenarios, and in comparing and optimizing the current proposed strategies.
PubDate: THU, 30 MAY 2024 09:16:24 -04
Issue No: Vol. 6, No. 3 (2024)
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- Analysis of Fatigue-Induced Compensatory Movements in Bicep Curls: Gaining
Insights for the Deployment of Wearable Sensors-
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Authors: Ming Xuan Chua;Yoshiro Okubo;Shuhua Peng;Thanh Nho Do;Chun Hui Wang;Liao Wu;
Pages: 1147 - 1157
Abstract: A common challenge in Bicep Curls rehabilitation is muscle compensation, where patients adopt alternative movement patterns when the primary muscle group cannot act due to injury or fatigue, significantly decreasing the effectiveness of rehabilitation efforts. The problem is exacerbated by the growing trend toward transitioning from in-clinic to home-based rehabilitation, where constant monitoring and correction by physiotherapists are limited. Developing wearable sensors capable of detecting muscle compensation becomes crucial to address this challenge. This study aims to gain insights into the optimal deployment of wearable sensors through a comprehensive study of muscle compensation in Bicep Curls. We collect upper limb joint kinematics and surface electromyography signals (sEMG) from eight muscles in 12 healthy subjects during standard and fatigue stages. Two muscle synergies are derived from sEMG signals and are analyzed comprehensively along with joint kinematics. Our findings reveal a shift in the relative contribution of forearm muscles to shoulder muscles, accompanied by a significant increase in activation amplitude for both synergies. Additionally, more pronounced movement was observed at the shoulder joint during fatigue. These results suggest focusing on the shoulder muscle activities and joint motions when deploying wearable sensors to effectively detect compensatory movements.
PubDate: THU, 30 MAY 2024 09:16:24 -04
Issue No: Vol. 6, No. 3 (2024)
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- On the OTHER Hand: A Bilateral, Reconfigurable Hand Exoskeleton With
Opposable Thumbs for Use With Upper Limb Exoskeletons-
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Authors: Peter Walker Ferguson;Jianwei Sun;Ji Ma;Joel Perry;Jacob Rosen;
Pages: 1158 - 1169
Abstract: This study aims to document the design of the OTHER Hand: a novel bilateral, reconfigurable, hand exoskeleton with opposable thumbs for use with upper limb exoskeletons. Intended for grasp research and rehabilitation with an emphasis on stroke, the OTHER Hand is designed as a one-size-fits-all system that can enable most of the common prehensile grasps and hand postures performed in activities of daily living. The capacity of the system to perform such grasps and postures is experimentally demonstrated by an average 94% normalized Grasping Ability Score across thirteen subjects using the Anthropomorphic Hand Assessment Protocol. This score demonstrates near-unhindered grasping performance for individuals without hand impairments wearing the OTHER Hand.
PubDate: FRI, 05 JUL 2024 09:16:12 -04
Issue No: Vol. 6, No. 3 (2024)
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- Combining Functional Electrical Stimulation (FES) to Elicit Hand Movements
and a Mechanical Orthosis to Passively Maintain Wrist and Fingers Position
in Individuals With Tetraplegia: A Feasibility Test-
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Authors: Clément Trotobas;Fernanda M. Rodrigues Martins Ferreira;João Paulo Fernandes Bonfim;Maria Rosália de Faria Moraes;Adriana Maria Valladão Novais Van Pette;Henrique Resende Martins;Charles Fattal;Christine Azevedo Coste;
Pages: 1170 - 1179
Abstract: We have developed a new approach to assist prehension by combining functional electrical stimulation (FES) and a motorized orthosis: ORTHYB. The aim was to induce movements of fingers, thumb, and wrist joints by activating muscles using surface FES and locking joints in desired positions using electric motors, to reduce muscle fatigue and enable prolonged grasping of objects. Another hypothesis was that the mechanical orthosis would improve grip quality by constraining joint positioning and guiding movements. The functionality and acceptability of this hybrid orthosis were tested on five participants with upper-limb paralysis due to spinal cord injury. The evaluation was carried out by monitoring the quality of grip for 30 seconds on 3 different objects; perceived effort using the Borg RPE (Rating of Perceived Exertion) scale; pain using visual analog scale (VAS); acceptability using QUEST (Quebec User Evaluation of Satisfaction Technology with Assistive Technology) scale and SUS (System Usability Scale). Preliminary results indicate that the hybrid orthosis provides added value compared to FES alone. The scores obtained in terms of functionality were in most of the trials greater than or equal to those obtained with FES alone. Object grasping was possible for 30 seconds without muscular fatigue affecting grip quality.
PubDate: MON, 01 JUL 2024 09:16:54 -04
Issue No: Vol. 6, No. 3 (2024)
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- Hybrid Rigid-Soft and Pneumatic-Electromechanical Exoskeleton for
Multi-Joint Lower Limb Assistance-
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Authors: Luka Mišković;Enrica Tricomi;Xiaohui Zhang;Francesco Missiroli;Kristina Krstanović;Tadej Petrič;Lorenzo Masia;
Pages: 1180 - 1189
Abstract: Human augmentation typically employs either rigid exoskeletons or soft exosuits. Rigid exoskeletons enhance stability and weight support through load-bearing frames and direct joint torque. Conversely, soft exosuits, devoid of rigid frames, utilize proximally positioned actuators and tendons to transmit forces to textile parts affixed to limbs, thereby enhancing adaptability and simplifying mechanics. To exploit the benefits of both, this study introduces a multi-joint hybrid-assisted device that combines a soft tendon-driven hip exosuit with a rigid pneumatic knee exoskeleton. The hip joint, featuring three active degrees of freedom, is assisted during the swing by the exosuit to minimize kinematic restrictions, mechanical complexity, and weight. The knee joint, with its single active degree of freedom, receives assistance during the stance from the rigid knee exoskeleton, pneumatically actuated, ensuring inherent knee compliance during load response. The study investigates the hybrid system’s impact on metabolic cost, muscle activity, and kinematics in four conditions (unassisted, hip-assisted, knee-assisted, and hybrid-assisted) with seven healthy subjects on an inclined treadmill (15° at 3 km/h). Findings indicate that hybrid assistance yields the greatest significant metabolic reductions, followed by hip assistance and knee-only assistance, with assisted muscles exhibiting significantly reduced activity and minimal impact on kinematics.
PubDate: MON, 01 JUL 2024 09:16:54 -04
Issue No: Vol. 6, No. 3 (2024)
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- MyKnee: Mechatronic Design of a Novel Powered Variable Stiffness
Prosthetic Knee-
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Authors: Gregorio Tagliabue;Vishal Raveendranathan;Amedeo Gariboldi;Lennard Y. Hut;Andrea Zucchelli;Raffaella Carloni;
Pages: 1190 - 1201
Abstract: Powered prosthetic legs have the potential of significantly enhancing the mobility, independence, and overall quality of life of individuals with lower-limb amputation. Unfortunately, powered prosthesis are followed by the issue of their weight and limited battery life when compared to passive or semi-active prosthesis, which, conversely, lack of complex movement capabilities. In this paper, we present an innovative design and the development of a powered prosthetic knee joint, which is actuated by means of a compact variable stiffness actuator. This innovative and promising technology can provide adaptability to different activities of daily living, while also ensuring energy efficiency and maintaining a lightweight design. The key feature of this novel powered knee joint lies in the use of a mechanism that can vary the stiffness of the joint through newly designed non-linear elastic elements. By applying advanced finite element analysis in the design process, a robust device has been realized that could readily comply with the ISO 10328.2016 standard for structural integrity. This made the knee joint suitable for future clinical trials with people with above-knee amputation.
PubDate: THU, 30 MAY 2024 09:16:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Enhanced EMG-Based Hand Gesture Classification in Real-World Scenarios:
Mitigating Dynamic Factors With Tempo-Spatial Wavelet Transform and Deep
Learning-
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Authors: Parul Rani;Sidharth Pancholi;Vikash Shaw;Manfredo Atzori;Sanjeev Kumar;
Pages: 1202 - 1211
Abstract: Dynamic factors, like limb position changes and electrode shifting, significantly impact the performance of EMG-based hand gesture classification as the transition is made from a laboratory-controlled environment to real-life scenarios. Traditionally, researchers have employed conventional wavelet transform methods to improve classification performance. This study compares a tempo-spatial technique that utilizes the wavelet multiresolution method and compares it with the conventional wavelet transform using eight machine learning algorithms. Two public datasets are utilized. DB1 comprising ideal conditions with a range of limb positions, while DB2 incorporates dynamic factors like electrode shifting and muscle fatigue. The training/testing involves two cases: one using single-position data and other with multiple positions. Results demonstrate that the Deep Neural Network (DNN) classifier outperforms others in classification accuracy. Proposed technique achieves mean accuracies of 84.07% (DB1) and 68.15% (DB2), while conventional wavelet transform methods achieve 79.39% (DB1) and 53.48% (DB2) for single-position DNN training. For multiple positions, particularly two limb positions, the proposed technique achieves mean accuracies of 94.43% (DB1) and 73.79% (DB2), compared to conventional wavelet transform, which achieves 84.38% (DB1) and 51.98% (DB2) with DNN. Paired t-tests (p-value
PubDate: MON, 03 JUN 2024 09:17:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Training Explainable and Effective Multi-DoF EMG Decoder Using Additive
1-DoF EMG-
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Authors: Yangyang Yuan;Chenyun Dai;Jiahao Fan;Chihhong Chou;Jionghui Liu;Xinyu Jiang;
Pages: 1212 - 1219
Abstract: Human hands can execute intricate and dexterous control of diverse objects. Decoding hand motions, especially estimating the force of each individual finger via surface electromyography (sEMG), is an essential step in intuitive and dexterous control of prosthetics, exoskeletons and more various human-machine systems. Previous sEMG decoders lack explainability and show degraded performances in decoding finger forces with multiple degrees-of-freedom (DoFs). When developing a multi-DoF EMG decoder, the combinations of various forces levels exerted by different fingers are too numerous to be exhaustively enumerate. In our work, we utilized the data of 1-DoF finger activation to generate synthetic N-DoF sEMG data with a straightforward additive mixup data augmentation approach, which overlays 1-DoF sEMG signals and finger force labels. The basic assumption of our method is the additive property of sEMG associated with different DoFs. With the synthetic N-DoF sEMG data, we then developed N-DoF EMG-force models via the highly explainable deep forest built on simple and transparent decision trees. With data augmentation using only 1-DoF sEMG data, the regression error reduced by ~20% of the baseline level (without data augmentation). More significantly, the explainability of the deep forest suggested that, the crucial electrodes in the decision making process of the 2-DoF deep forest are essentially a linear superposition of the counterparts in the 1-DoF deep forest.
PubDate: MON, 03 JUN 2024 09:17:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Cable-Driven Light-Weighting and Portable System for Robotic Medical
Ultrasound Imaging-
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Authors: Guochen Ning;Jie Wang;Hongen Liao;
Pages: 1220 - 1231
Abstract: Robotic ultrasound imaging systems (RUSs) have captured significant interest owing to their potential to facilitate autonomous ultrasound imaging. However, existing RUSs built upon robotic systems oriented towards conventional manufacturing struggle to navigate the variable and dynamic clinical environments. We introduce a portable and lightweight RUS designed to enhance adaptability for ultrasound imaging tasks. The proposed system features multiple parallel rings and bearings, affording it four degrees-of-freedom for precise posture control. Further enhancing its adaptability, the actuators are isolated from the mechanism and connected by a cable-sheath mechanism, resulting in a mere 519g lightweight structure that attaches to the body. Quantitative assessments indicate that within a vast workspace of 981 cm3, the posture control precision of the probe is measured at $1.32\pm 0.1$ mm and [ $1.8\pm 1.1^{\circ }$ , $1.9\pm 2.2^{\circ }$ , $0.8~\pm 0.8^{\circ }$ ]. The maximum compression force measured for the probe is 14.5 N. The quantitative evaluation results show that the system can attach to various parts of the human body for image acquisition. In addition, the proposed system excels in performing stable scanning procedures even in rapidly changing dynamic environments. Our system can realize imaging tasks with a much lighter structure and has the potential to be applied to more complex scenarios.
PubDate: WED, 03 JUL 2024 09:16:55 -04
Issue No: Vol. 6, No. 3 (2024)
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- Bayesian Algorithm-Based Force Profiles Optimization of Hip-Assistive Soft
Exosuits Under Variable Walking Speeds-
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Authors: Qiang Chen;Jiaxin Wang;Qian Xiang;Shijie Guo;
Pages: 1232 - 1244
Abstract: Relevant research highlights humans’ capacity to continuously adapt their walking speed to minimize metabolic energy consumption during overground free walking. Past studies have shown that soft exosuits assisting in hip flexion and extension can reduce metabolic costs and regulate gait parameters during human locomotion. This emphasizes the need to fine-tune hip exosuit parameters to align with walking speed, thereby enhancing metabolic efficiency. This study aims to optimize assistive force parameters of hip exosuits across different walking speeds, providing insights for optimizing force profiles in outdoor walking. We employed a human-in-the-loop approach with Bayesian optimization to determine optimal force profiles for hip assistance. Six subjects performed treadmill walking at four fixed speeds (0.84, 1.16, 1.48, and 1.8 m/s), optimizing control parameters for each speed and establishing a Bayesian experience (BXE) linking walking speed to optimal parameters. Furthermore, we developed a real-time force optimization controller based on the BXE for adjusting the force parameters of assistance. Outdoor walking experiments with the same subjects showed that BXE-optimized profiles significantly reduced metabolic costs compared to fixed profiles. This study underscores the importance of optimizing assistive forces for varying walking speeds in humans.
PubDate: MON, 03 JUN 2024 09:17:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Real-Time Insertion Depth Tracking of Cochlear Implant Electrode Array
With Bipolar Complex Impedance and Machine Intelligence-
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Authors: Nauman Hafeez;Nikolaos Boulgouris;Philip Begg;Richard Irving;Chris Coulson;Hao Wu;Huan Jia;Xinli Du;
Pages: 1245 - 1255
Abstract: Cochlear implants have significantly improved hearing for many as the most successful prosthesis, however, hearing outcomes vary. Uncertainty during electrode array (EA) insertion, including trauma and depth control, is one factor. To minimize radiation exposure from imaging methods like CT scans, this in-vitro study investigates the use of bipolar electrode impedance and artificial intelligent models to determine EA insertion depth. Complex impedance data was collected by inserting a commercial EA into a scaled-up 2D scala tympani model using a robotic feeder system. A support vector machine model produced a 98% classification accuracy for final insertion depth estimation. A CNN-LSTM hybrid model yielded 0.85 R-squared and 1.72 mm mean absolute error in depth estimation at each millimeter during a 25 mm insertion. This approach to depth assessment based on impedance may help with cochlear implant procedures and find use in other medical implant applications.
PubDate: THU, 30 MAY 2024 09:16:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- Needle Steering Controller Design for Flexible Steerable Needle Utilizing
Robust Backstepping Control Strategy-
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Authors: Kaushik Halder;M. Felix Orlando;
Pages: 1256 - 1269
Abstract: In Minimal Invasive Surgery (MIS), steerable flexible needles are commonly utilized as surgical tools to improve target-reaching accuracy. Nevertheless, challenges like tissue deformation, tissue inhomogeneity, and noisy sensory measurements can lead to inaccuracies in needle-tip positioning within the tissue domain. Therefore, to ensure precise needle placement in tissue region, designing a robust non-linear closed-loop needle steering control becomes a crucial aspect in percutaneous intervention procedures. Consequently, in pursuit of accurate and precise needle placement within tissue, various controller methodologies are evident in current literature. However, to address the complexity associated with the design of existing control strategies, this study introduces a robust non-linear needle steering controller within the tissue environment, with the goal of stabilizing the needle within a designated plane. Our proposed needle steering technique incorporates the backstepping based controller that involves the splitting of entire needle kinematic model into several smaller designs while ensuring closed-loop stability through Lyapunov stability analyses. Efficacy of the devised needle steering approach is validated by comparing it with existing control techniques through extensive simulation studies, specifically focusing on needle placement in both 2D and 3D planes. Furthermore, experimental validation is performed involving brachytherapy needle with both artificial tissue phantom and biological tissue.
PubDate: MON, 01 JUL 2024 09:16:53 -04
Issue No: Vol. 6, No. 3 (2024)
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- Fourier Decomposition-Based Automated Classification of Healthy, COPD, and
Asthma Using Single-Channel Lung Sounds-
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Authors: Vaibhav Koshta;Bikesh Kumar Singh;Ajoy K. Behera;Ranganath T. G.;
Pages: 1270 - 1284
Abstract: Subjective discrimination of asthma and Chronic Obstructive Pulmonary Disease (COPD) is challenging as they share overlapping symptoms and are subject to personal interpretation. Hence, there is a demand for an alternative diagnostic system devoid of any subjective interference. The current study introduces Fourier Decomposition Method (FDM) based models utilizing Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) to identify patients with asthma and COPD by analyzing lung sound signals. The signals were decomposed into Fourier intrinsic band functions (FIBF) using three filter banks: dyadic, equal energy, and uniform band. Four statistical attributes, namely: Shannon entropy, log entropy, median absolute deviation and kurtosis, are calculated from relevant FIBF. Support vector machine (SVM), k-nearest neighbor (kNN) and ensemble classifier (EC) optimized with Bayesian optimization are used for classification of asthma vs COPD and normal vs adventitious sound, respectively. The highest accuracies achieved using DCT with 10-fold cross-validation are as follows: 99.4% (Asthma vs COPD), 99.1% (Asthma vs COPD vs Normal), 99.4% (COPD vs Normal) and 99.7% (Asthma vs Normal). Similarly, the highest accuracies reported by DFT with 10-fold cross-validation are: 99.4% (Asthma vs COPD), 99.6% (Asthma vs COPD vs Normal), 99.4% (COPD vs Normal) and 99.8% (Asthma vs Normal).
PubDate: MON, 03 JUN 2024 09:17:23 -04
Issue No: Vol. 6, No. 3 (2024)
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- GHMM: Learning Generative Hybrid Mixture Models for Generalized Point Set
Registration in Computer-Assisted Orthopedic Surgery-
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Authors: Zhengyan Zhang;Ang Zhang;Jiewen Lai;Hongliang Ren;Rui Song;Yibin Li;Max Q.-H. Meng;Zhe Min;
Pages: 1285 - 1295
Abstract: In computer-assisted orthopedic surgery (CAOS), the overlay of pre-operative information onto the surgical scene is achieved through the registration of pre-operative 3D models with the intra-operative surface. The accuracy and robustness of this registration are crucial for effective interventional guidance. To enhance these qualities in CAOS, we explore the use of normal vectors and the concept of joint registration of two point sets, to simultaneously utilize more useful geometrical information and consider noise and outliers in both pre-operative and intra-operative spaces. We present a novel end-to-end hybrid learning-based registration method for CAOS by utilizing generalized point sets that consist of positional and normal vectors, which are considered to be generated from an unknown Generative Hybrid Mixture Model (GHMM) composed of Gaussian Mixture Models (GMMs) and Fisher Mixture Models (FMMs). The joint registration is cast as a maximum likelihood estimation (MLE) problem that aims to minimize the distances between the generalized points and the hybrid distributions. Our proposed approach, termed GHMM, has been extensively validated on various medical data sets (i.e., 291 human femur and 260 hip models) and the public dataset ModelNet40, outperforming state-of-the-art registration methods significantly $(\text {p-value}\lt 0.01)$ . This suggests the potential of GHMM for applications in orthopedic surgical navigation and object localization. Furthermore, even under different noises and lower overlap ratio conditions, all evaluation metrics of GHMM are superior to other probabilistic methods, demonstrating GHMM’s great capability to handle the partial-to-full registration problem and robustness to disturbances.
PubDate: THU, 30 MAY 2024 09:16:24 -04
Issue No: Vol. 6, No. 3 (2024)
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