JournalTOCs

IEEE Transactions on Medical Robotics and Bionics
Number of Followers: 3

Hybrid journal (It can contain Open Access articles)
ISSN (Online) 2576-3202
Published by IEEE

[228 journals]

IEEE Transactions on Medical Robotics and Bionics Publication Information
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  Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
IEEE Robotics and Automation Society Information
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  Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
IEEE Transactions on Medical Robotics and Bionics Information for Authors
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  Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Guest Editorial Special Section on Surgical Vision, Navigation, and
Robotics
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  Authors: Xiongbiao Luo;Danail Stoyanov;Nobuhiko Hata;Alejandro F. Frangi;Russell H. Taylor;Terry M. Peters;
  Pages: 2 - 4
  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: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Incorporating Uncertainty Into Path Planning for Minimally Invasive
Robotic Neurosurgery
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  Authors: Sarah Frisken;Jie Luo;Nazim Haouchine;Steve Pieper;Yixin Wang;William M. Wells;Alexandra J. Golby;
  Pages: 5 - 16
  Abstract: New robotics devices can provide minimally invasive access to deep lesions in the brain through small openings in the skull, which can reduce infection risk and improve recovery times when compared to larger craniotomies. Several groups have introduced semi and fully automatic path planning tools to optimize lesion access while avoiding damage to critical structures along the path. These tools utilize preoperative imaging and intraoperative neuronavigation to plan and execute lesion access. However, several factors can introduce uncertainty into path planning, including segmentation, patient-to-image registration, brain shift during surgery, and mechanical uncertainty in the robotic device during path following. Most systems handle uncertainty by adding a ‘safety margin’ (typically 2–3 mm) around structures to be avoided or around the path. However, safety margins are somewhat arbitrary. We propose using more rigorous models of uncertainty during path planning. We explore two sources of uncertainty in path planning, namely segmentation uncertainty and uncertainty due to brain shift, propose a method for computing brain shift uncertainty, show how to combine these uncertainties into a single risk volume, and provide clinical motivation by presenting our approach in the context of a path planning system for robotic neurosurgery designed for treating mesial temporal lobe epilepsy.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
A Preliminary Exploration to Make Stereotactic Surgery Robots Aware of the
Semantic 2D/3D Working Scene
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  Authors: Liang Li;Pengfei Feng;Hui Ding;Guangzhi Wang;
  Pages: 17 - 27
  Abstract: Scene perceptual ability is key to developing autonomy and intelligence in surgical robots. This study helps stereotactic surgical robots detect and segment key objects in unstructured surgical scenes. First, we construct a neurosurgery robot working scene dataset. Next, we propose a 2Dimage-scene-aware pipeline that integrates a Mask R-CNN (mask region-based convolutional neural network) with a conditional random field and a superpixel method; the pipeline detects and segments key objects, such as the patient’s head, head frame, and body. Then, we establish a multiview projection voting and supervoxel fusion pipeline that extracts further information from a 3D point cloud scene. The proposed method was tested in different clinical scenarios, and the results show that the method can detect and segment specific surgical objects and achieves comparable accuracy and stability on both 2D images and 3D point cloud data. The average precision (AP) and average 2D and 3D Dice scores for the patient’s head were 97.65, 91.6, and 92.6, respectively. Better segmentation performances can be achieved when the data-based neural network method further integrates the traditional color and contour-based image processing methods. The proposed solution allows stereotactic surgical robots to better understand their surroundings, provides semantic information useful for subsequent tasks, and lays a foundation for autonomous stereotactic surgical robots.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Pre-Clinical Development of Robot-Assisted Ventriculoscopy for 3-D Image
Reconstruction and Guidance of Deep Brain Neurosurgery
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  Authors: Prasad Vagdargi;Ali Uneri;Craig K. Jones;Pengwei Wu;Runze Han;Mark G. Luciano;William S. Anderson;Patrick A. Helm;Gregory D. Hager;Jeffrey H. Siewerdsen;
  Pages: 28 - 37
  Abstract: Conventional neuro-navigation can be challenged in targeting deep brain structures via transventricular neuroendoscopy due to unresolved geometric error following soft-tissue deformation. Current robot-assisted endoscopy techniques are fairly limited, primarily serving to planned trajectories and provide a stable scope holder. We report the implementation of a robot-assisted ventriculoscopy (RAV) system for 3D reconstruction, registration, and augmentation of the neuroendoscopic scene with intraoperative imaging, enabling guidance even in the presence of tissue deformation and providing visualization of structures beyond the endoscopic field-of-view. Phantom studies were performed to quantitatively evaluate image sampling requirements, registration accuracy, and computational runtime for two reconstruction methods and a variety of clinically relevant ventriculoscope trajectories. A median target registration error of 1.2 mm was achieved with an update rate of 2.34 frames per second, validating the RAV concept and motivating translation to future clinical studies.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Design and Optimization of a 3D Printed Distal Flexible Joint for
Endoscopic Surgery
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  Authors: Yongxiang Song;Shuxin Wang;Xiangyu Luo;Chaoyang Shi;
  Pages: 38 - 49
  Abstract: This paper presents a flexible joint with excellent motion range, outstanding constant curvature characteristics, tiny hysteresis, and high loading capacity to support distal operation and observation for flexible endoscopy and natural orifice transluminal endoscopic surgery (NOTES). It adopts a rigid-flexible coupling design based on the 3D printing technique and mainly consists of multiple helical segments and rigid segments in a staggered arrangement. This flexible joint relies on the uniform stress distribution on the helical segments to generate a smooth bending shape and is configurated with a central hollow channel to carry surgical instruments instead of the central backbone configuration. Both kinematic modeling and simplified static modeling have been derived. Design optimization has been subsequently conducted to investigate the structural parameters for further performance improvement. Both flexible joints in a single-section and two-section configurations have been utilized for experiments to analyze and validate their performances. The experimental results show that the proposed flexible joints achieve excellent distal end positioning accuracy within [−180°, 180°] with a small distal average positioning error of 2.20% and a tiny hysteresis error of 0.95%. The modeling error between the derived kinematics and experimental results is only 3.8%. Meanwhile, the proposed joint can withstand a large surgical operation loading of 5N. The obstacle avoidance and colorectal phantom experiments have also been performed to demonstrate the great potentials for endoscopic surgery and NOTES.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Surgical Skill Assessment Based on Dynamic Warping Manipulations
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  Authors: Xiao-Hu Zhou;Xiao-Liang Xie;Shi-Qi Liu;Zhen-Qiu Feng;Mei-Jiang Gui;Jin-Li Wang;Hao Li;Tian-Yu Xiang;Gui-Bin Bian;Zeng-Guang Hou;
  Pages: 50 - 61
  Abstract: Percutaneous coronary intervention (PCI) has become a popular treatment for coronary artery disease. Highly dexterous skills are necessary to procedure success. However, few effective methods can be applied to PCI skill assessment. In this study, ten interventional cardiologists (four experts and six novices) were recruited. In vivo studies were performed via delivering a medical guidewire into distal left circumflex artery (target vessel I) and obtuse marginal artery (target vessel II) of a porcine model. Regarded as a type of manipulation data, the guidewire motion is simultaneously acquired with an electromagnetic (EM) sensor attached to guidewire tail. To address the deficiency of conventional dynamic time warping (DTW) limited to two-sequence matching, a novel warping algorithm is proposed to match multiple manipulation data. Then the intra-similarity is calculated to evaluate consistencies among each subject’s different manipulations, while the inter-similarity is further analyzed to find skill differences among different subjects. Extensive statistical analysis demonstrates that the proposed algorithm can effectively distinguish between the manipulations made by different skill-level subjects with significant differences on the target vessel I ( $P = 3.25times 10^{-4}$ ) and II ( $P = 7.30times 10^{-3}$ ). These promising results show the proposed technique’s great potential to facilitate skill assessment in clinical practice and skill learning in surgical robotics.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Towards Surgical Tools Detection and Operative Skill Assessment Based on
Deep Learning
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  Authors: Yuying Liu;Zijian Zhao;Pan Shi;Feng Li;
  Pages: 62 - 71
  Abstract: Surgical tool detection and automatic operation skill assessment (AOSA) have important and extensive application scenarios in minimally invasive surgery (MIS). However, most of the deep learning methods currently used for surgical tool detection cannot achieve a good balance between speed and accuracy. We propose a new real-time detection algorithm for MIS tools, which called depth-wise separable convolutional network with convolutional long short-term memory (DSCNet-CLSTM). The network combines the advantages of the one-stage multi-scale feature maps’ concept in the state-of-art detection methods and the convolutional variant of the LSTM. This combination makes full use of the complementary information of spatial and temporal features learned from the laparoscopic video frames. In addition, we have established AOSA system. By processing the output information of the MIS tool detection algorithm, we can obtain the operation tool usage information in the laparoscopic video. Then, this information is taken as the dataset of AOSA system, which makes it possible to realize AOSA based on convolutional neural network (CNN). The proposed method achieved the mAP values of 100, 90.07 and 89.96% at a speed of 50.0 fps for the Endovis Challenge, ATLAS Dione, and Cholec80-locations datasets, respectively. The AOSA system obtained the mean squared error of 2.281, 0.987, 0.069, and 0.009 for the action timeline, heat map, motion trajectory, and all, respectively. The experimental results prove that the framework can be efficiently trained in an end-to-end manner and improves the algorithm’s detection accuracy and speed. Finally, we verify the feasibility of the designed AOSA system through experiments.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Anthropomorphic Dual-Arm Coordinated Control for a Single-Port Surgical
Robot Based on Dual-Step Optimization
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  Authors: Weibang Bai;Ziwei Wang;Qixin Cao;Hiroshi Yokoi;Masakatsu G. Fujie;Eric M. Yeatman;Guang-Zhong Yang;
  Pages: 72 - 84
  Abstract: Effective teleoperation of the small-scale and highly-integrated robots for single-port surgery (SPS) imposes unique control and human-robot interaction challenges. Traditional isometric teleoperation schemes mainly focus on end-to-end trajectory mapping, which is problematic when applied to SPS robotic control, especially for dual-arm coordinated operation. Inspired by the human arm configuration in boxing maneuvers, an optimized anthropomorphic coordinated control strategy based on a dual-step optimization approach is proposed. Theoretical derivation and solvability of the problem are addressed, and the effectiveness of the method is further demonstrated in detailed simulation and in-vitro experiments. The proposed control strategy has been shown to perform dexterous SPS bimanual manipulation more effectively, involving less instrument-interference and is free from singularities, thereby improving the safety and efficiency of SPS operations.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Magnetically Assisted Robotic Fetal Surgery for the Treatment of Spina
Bifida
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  Authors: Simone Gervasoni;Jonas Lussi;Silvia Viviani;Quentin Boehler;Nicole Ochsenbein;Ueli Moehrlen;Bradley J. Nelson;
  Pages: 85 - 93
  Abstract: Spina bifida is a congenital defect that occurs on the vertebral spine of a fetus. The most severe form causes exposure of the spinal cord and spinal nerve and has important repercussions on the life of the newborn child. Current prenatal operative procedures require laparotomy of the abdomen as well as hysterotomy of the uterus, which can result in severe consequences and risks for the mother. Here, we propose a robotically assisted endoscopic procedure based on magnetically steerable catheters to treat spina bifida defects in a minimally invasive way. The procedure can be performed in a fully remote manner using magnetic guidance and a haptic controller. Four custom magnetic catheter designs are presented to image, sever, grasp and close the lesion on the fetus back. We demonstrate our approach in vitro using phantom models of the abdomen, the uterus, and the defected fetus.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Robotic Tissue Sampling for Safe Post-Mortem Biopsy in Infectious Corpses
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  Authors: Maximilian Neidhardt;Stefan Gerlach;Robin Mieling;Max-Heinrich Laves;Thorben Weiß;Martin Gromniak;Antonia Fitzek;Dustin Möbius;Inga Kniep;Alexandra Ron;Julia Schädler;Axel Heinemann;Klaus Püschel;Benjamin Ondruschka;Alexander Schlaefer;
  Pages: 94 - 105
  Abstract: In pathology and legal medicine, the histopathological and microbiological analysis of tissue samples from infected deceased is a valuable information for developing treatment strategies during a pandemic such as COVID-19. However, a conventional autopsy carries the risk of disease transmission and may be rejected by relatives. We propose minimally invasive biopsy with robot assistance under CT guidance to minimize the risk of disease transmission during tissue sampling and to improve accuracy. A flexible robotic system for biopsy sampling is presented, which is applied to human corpses placed inside protective body bags. An automatic planning and decision system estimates optimal insertion point. Heat maps projected onto the segmented skin visualize the distance and angle of insertions and estimate the minimum cost of a puncture while avoiding bone collisions. Further, we test multiple insertion paths concerning feasibility and collisions. A custom end effector is designed for inserting needles and extracting tissue samples under robotic guidance. Our robotic post-mortem biopsy (RPMB) system is evaluated in a study during the COVID-19 pandemic on 20 corpses and 10 tissue targets, 5 of them being infected with SARS-CoV-2. The mean planning time including robot path planning is 5.72±167s. Mean needle placement accuracy is 7.19± 422mm.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
GuLiM: A Hybrid Motion Mapping Technique for Teleoperation of Medical
Assistive Robot in Combating the COVID-19 Pandemic
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  Authors: Honghao Lv;Depeng Kong;Gaoyang Pang;Baicun Wang;Zhangwei Yu;Zhibo Pang;Geng Yang;
  Pages: 106 - 117
  Abstract: Driven by the demand to largely mitigate nosocomial infection problems in combating the coronavirus disease 2019 (COVID-19) pandemic, the trend of developing technologies for teleoperation of medical assistive robots is emerging. However, traditional teleoperation of robots requires professional training and sophisticated manipulation, imposing a burden on healthcare workers, taking a long time to deploy, and conflicting the urgent demand for a timely and effective response to the pandemic. This paper presents a novel motion synchronization method enabled by the hybrid mapping technique of hand gesture and upper-limb motion (GuLiM). It tackles a limitation that the existing motion mapping scheme has to be customized according to the kinematic configuration of operators. The operator awakes the robot from any initial pose state without extra calibration procedure, thereby reducing operational complexity and relieving unnecessary pre-training, making it user-friendly for healthcare workers to master teleoperation skills. Experimenting with robotic grasping tasks verifies the outperformance of the proposed GuLiM method compared with the traditional direct mapping method. Moreover, a field investigation of GuLiM illustrates its potential for the teleoperation of medical assistive robots in the isolation ward as the Second Body of healthcare workers for telehealthcare, avoiding exposure of healthcare workers to the COVID-19.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
A Systematic Literature Review of Intent Sensing for Control of Medical
Devices
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  Authors: Joseph Russell;Jeroen Bergmann;
  Pages: 118 - 129
  Abstract: The usefulness of medical devices, which require user input, is often limited by the control schemes that operate them. The recognition of user intent could enable far more intuitive control schemes that respond automatically to what the user wants the device to do. This paper provides a definition for intent, and then aims to systematically review current methods for sensing intent. It compares the accuracy of different methods and discusses how they might be combined. A systematic literature search was performed using IEEE Xplore, PubMed and Web of Science databases. 2311 papers were considered, reduced to 155 after review. All selected papers were assessed for quality using a checklist. The results identified and compared 15 sensing modalities used for intent sensing in a range of situations and applications that broadly fell into 12 distinct categories, with highly varying levels of accuracy. Several papers reached accuracy levels that could be suitable for everyday clinical application, but most work done on intent sensing to date has focused on activity transition classification, with fewer papers addressing task goal interference or predicting future actions. Further work can focus on the implementation of these kind of methods into a combined, context-aware intent-sensing control system.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous
Spinal Sonography Using a Shadow-Aware Dual-Agent Framework
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  Authors: Keyu Li;Yangxin Xu;Jian Wang;Dong Ni;Li Liu;Max Q.-H. Meng;
  Pages: 130 - 144
  Abstract: Ultrasound (US) imaging is commonly used to assist in the diagnosis and interventions of spine diseases, while the standardized US acquisitions performed by manually operating the probe require substantial experience and training of sonographers. In this work, we propose a novel dual-agent framework that integrates a reinforcement learning (RL) agent and a deep learning (DL) agent to jointly determine the movement of the US probe based on the real-time US images, in order to mimic the decision-making process of an expert sonographer to achieve autonomous standard view acquisitions in spinal sonography. Moreover, inspired by the nature of US propagation and the characteristics of the spinal anatomy, we introduce a view-specific acoustic shadow reward to utilize the shadow information to implicitly guide the navigation of the probe toward different standard views of the spine. Our method is validated in both quantitative and qualitative experiments in a simulation environment built with US data acquired from 17 volunteers. The average navigation accuracy toward different standard views achieves $5.18mm/5.25^{circ }$ and $12.87mm/17.49^{circ }$ in the intra- and inter-subject settings, respectively. The results demonstrate that our method can effectively interpret the US images and navigate the probe to acquire multiple standard views of the spine.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Development of a Distal Tri-Axial Force Sensor for Minimally Invasive
Surgical Palpation
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  Authors: Zhongxin Tang;Shuxin Wang;Ming Li;Chaoyang Shi;
  Pages: 145 - 155
  Abstract: The current study highlights the development of a novel and high-accuracy Fiber Bragg Grating (FBG)-enabled tri-axial distal force sensor for minimally invasive surgical palpation. This tri-axial sensor designs the primary flexure/elastomer consisting of axial and radial force-sensitive structures in a serial configuration, based on the method of freedom and constraint topology (FACT) from the perspective of the mechanism. This method provides general guidelines for designing multi-dimensional sensor design, achieving excellent sensitivity and a large measurement range, depressing crosstalks and couplings among different axes, and keeping the sensitivity of the three directions at the same order of magnitude. Five tightly suspended optical fibers embedded with one FBG element each have been assembled with the proposed flexure. They are configured with one arranged at the axial flexure’s central line and four distributed along the circumference of the radial flexure. This tight suspension configuration generates uniform and constant strain distribution on the FBG element, thus improving resolution and repeatability and further avoiding FBG chirping. Finite element modeling (FEM)-based simulation has been conducted for performance investigation and design optimization to improve the sensor sensitivity. Static calibration experiments and in-vitro and ex-vivo palpation experiments have been performed to investigate the proposed design’s performances and feasibility. The optimized sensor prototype achieves excellent resolution values of 1.18mN and 1.81mN in the x- and y-directions within [−5N, 5N], and 2.61mN in the z-direction within [0, 5N], realizing the same order of sensitivity magnitude at each axis.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Autonomous Coordinated Control of the Light Guide for Positioning in
Vitreoretinal Surgery
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  Authors: Yuki Koyama;Murilo M. Marinho;Mamoru Mitsuishi;Kanako Harada;
  Pages: 156 - 171
  Abstract: Vitreoretinal surgery is challenging even for expert surgeons owing to the delicate target tissues and the diminutive workspace in the retina. In addition to improved dexterity and accuracy, robot assistance allows for (partial) task automation. In this work, we propose a strategy to automate the motion of the light guide with respect to the surgical instrument. This automation allows the instrument’s shadow to always be inside the microscopic view, which is an important cue for the accurate positioning of the instrument in the retina. We show simulations and experiments demonstrating that the proposed strategy is effective in a 700-point grid in the retina of a surgical phantom. Furthermore, we integrated the proposed strategy with image processing and succeeded in positioning the surgical instrument’s tip in the retina, relying on only the robot’s geometric information and microscopic images.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
A Surgical Robotic System for Long-Bone Fracture Alignment: Prototyping
and Cadaver Study
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  Authors: Marzieh S. Saeedi-Hosseiny;Fayez Alruwaili;Sean McMillan;Iulian Iordachita;Mohammad H. Abedin-Nasab;
  Pages: 172 - 182
  Abstract: In this paper, we design, develop, and validate a surgical robotic system, entitled Robossis, to assist long-bone fracture reduction, i.e., alignment, surgeries. Unlike traditional long-bone fracture surgeries, Robossis enables the surgeon to precisely align the fractured bone in the presence of large traction forces and torques. The proposed surgical system includes a novel 3-armed robot, a bone-gripping mechanism, and a master controller. The 6-DOF 3-armed wide-open parallel robot has a unique architecture, which facilitates positioning the bone inside the robot, providing a large workspace for surgical maneuvers. Kinematic analysis shows that the symmetric 3-armed mechanism provides a significant advantage over the Gough-Stewart platform, i.e., 15 times larger rotational workspace, which is a vital advantage for fracture alignment. Theoretical and experimental testing are performed to demonstrate Robossis performance, including high accuracy and force insertion capabilities. A successful cadaver test was performed using a Robossis prototype, which shows that guided by intraoperative X-ray imaging, Robossis is able to manipulate bone in all translational and rotational directions while encountering the muscle payload surrounding the femur. Robossis is designed to balance accuracy, payload, and workspace, and its innovative design presents major advantages over the existing robot designs for the reduction of long-bone fractures.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Development of the X-Perce—A Universal FBG-Based Force Sensing Kit for
Laparoscopic Surgical Robot
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  Authors: Chengjin Du;Dehao Wei;Han Wang;Weidong Wang;Jian Dong;Haitao Huo;Yingtian Li;
  Pages: 183 - 193
  Abstract: For surgical robot, an independent contact sensing system can always provide intuitive operation reference in parallel to non-contact information. As one of them, force sensing system developed based on fiber Bragg grating sensor (FBGs) has been at the forefront of gaining force information from surgical device in the past decade. Application span from vitrectomy, cerebral surgery to cardiac ablation yet still rarely seen in laparoscopic surgery. In this paper, we present the development of the X-Perce—a force sensing kit that can be universally deployed on the laparoscopic surgical robot. By the design of a 3D-printed biocompatible joint combining with six FBGs layout, the proposed kit is able to perform tri-axial force sensing with temperature compensation and dual-thread mechanism. Modular design and standardized integration process are able to reduce the cost of manufacture, which allow the X-Perce to be consumable and commercial. Optimization based on mechanical modelling is implemented to enhance the system performance. Testing and evaluation is conducted to assess the resolution, static indexes and tracking performance of the prototype. Overall, we provide here a mechanical force sensing solution for the instrument that can be integrated on the Da-Vinci-like laparoscopic robotic system, along with a complete design paradigm for FBG sensing system alike.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Shaping Individualized Impedance Landscapes for Gait Training via
Reinforcement Learning
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  Authors: Yufeng Zhang;Shuai Li;Karen J. Nolan;Damiano Zanotto;
  Pages: 194 - 205
  Abstract: Assist-as-needed (AAN) control aims at promoting therapeutic outcomes in robot-assisted rehabilitation by encouraging patients’ active participation. Impedance control is used by most AAN controllers to create a compliant force field around a target motion to ensure tracking accuracy while allowing moderate kinematic errors. However, since the parameters governing the shape of the force field are often tuned manually or adapted online based on simplistic assumptions about subjects’ learning abilities, the effectiveness of conventional AAN controllers may be limited. In this work, we propose a novel adaptive AAN controller that is capable of autonomously reshaping the force field in a phase-dependent manner according to each individual’s motor abilities and task requirements. The proposed controller consists of a modified Policy Improvement with Path Integral algorithm, a model-free, sampling-based reinforcement learning method that learns a subject-specific impedance landscape in real-time, and a hierarchical policy parameter evaluation structure that embeds the AAN paradigm by specifying performance-driven learning goals. The adaptability of the proposed control strategy to subjects’ motor responses and its ability to promote short-term motor adaptations are experimentally validated through treadmill training sessions with able-bodied subjects who learned altered gait patterns with the assistance of a powered ankle-foot orthosis.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Dynamic Parameter Identification of a Human-Exoskeleton System With the
Motor Torque Data
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  Authors: Yinbo Li;Xinyu Guan;Wei Li;Bernhard Penzlin;Kaiqi Liu;Ze Yang;Bing Liu;Steffen Leonhardt;Linhong Ji;
  Pages: 206 - 218
  Abstract: The objective of this paper is to identify dynamic parameters of the human-exoskeleton system (HES) with the motor torque data considering modeling the passive elastic joint torque of the user. Based on a lower limb exoskeleton prototype we recently developed, the dynamics of the joint actuators and two degrees of freedom link-based swing leg are firstly modeled. The passive elasticity of the human joint is modeled by double-exponential equations. Systematic experiments are then conducted, during which the motor current is measured to compute the motor torque for identification. Finally, the dynamic parameters of the joint actuators, the exoskeleton leg and the human-exoskeleton leg are successively identified. The identification is implemented through the off-line inverse dynamic model simulation with the measured motor torque data. A consistent tendency is found between the simulated motor torque after identification and the measured one. This demonstrates the validity of the identification method proposed. This paper provides a systemic method of identifying the HES with the motor torque data and enriches the experimental proof that the double-exponential model is feasible to describe the passive elastic joint torque.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Subject-Independent, Biological Hip Moment Estimation During Multimodal
Overground Ambulation Using Deep Learning
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  Authors: Dean D. Molinaro;Inseung Kang;Jonathan Camargo;Matthew C. Gombolay;Aaron J. Young;
  Pages: 219 - 229
  Abstract: Estimating biological joint moments using wearable sensors could enable out-of-lab biomechanical analyses and exoskeletons that assist throughout daily life. To realize these possibilities, this study introduced a subject-independent hip moment estimator using a temporal convolutional network (TCN) and validated its performance and generalizability during multimodal ambulation. Electrogoniometer and simulated IMU data from sixteen participants walking on level ground, ramps and stairs were used to evaluate our approach when benchmarked against a fully-connected neural network, a long short-term memory network, and a baseline method (i.e., using subject-average moment curves based on ambulation mode and gait phase). Additionally, the generalizability of our approach was evaluated by testing on ground slopes, stair heights, and gait transitions withheld during model training. The TCN outperformed the benchmark approaches on the hold-out data (p < 0.05), with an average RMSE of 0.131±0.018 Nm/kg and R2 of 0.880±0.030 during steady-state ambulation. When tested on the 20 leave-one-out slope and stair height conditions, the TCN significantly increased RMSE only on the steepest (+18°) incline (p < 0.05). Finally, the TCN RMSE and R2 was 0.152±0.027 Nm/kg and 0.786±0.055, respectively, during mode transitions. Thus, our approach accurately estimated hip moments and generalized to unseen gait contexts using data from three wearable sensors.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Simulated Performance of an Energy Storage and Return Prosthetic Ankle
Based on Cams and Miniature Hydraulics
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  Authors: Anna Pace;James Gardiner;Laurence Kenney;David Howard;
  Pages: 230 - 240
  Abstract: Prosthetic feet are limited in their ability to mimic the energy-recycling behavior of an intact ankle, negatively affecting lower-limb amputees’ gait in terms of metabolic cost and walking speed. To overcome these weaknesses, a novel prosthetic ankle based on hydraulics is described here. The ankle joint drives two cams, which in turn drive two hydraulic rams. One cam-ram system captures the negative work done from foot-flat until maximum dorsiflexion, by pumping oil into an accumulator, while the other returns positive work during push-off providing forward propulsion through fluid flowing from the accumulator to the ram. Simulation results are promising: of the total negative work done by the prosthetic ankle over the gait cycle (i.e., the maximum amount of energy available to be stored), 78% is returned, mainly during push-off; 14% is carried forward for future gait cycles; and 8% is lost. The estimated prosthesis height and mass are approximately ${26}.{5} {cm}$ and ${2}.{3} {kg}$ . Nonetheless, further work is necessary to realise a prototype for bench and in-vivo testing. By mimicking intact ankle torque and efficiently storing and returning energy at the ankle joint, this new design may contribute to reducing amputees’ metabolic cost of walking.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Learning a Generic Olfactory Search Strategy From Silk Moths by Deep
Inverse Reinforcement Learning
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  Authors: Cesar Hernandez-Reyes;Shunsuke Shigaki;Mayu Yamada;Takeshi Kondo;Daisuke Kurabayashi;
  Pages: 241 - 253
  Abstract: Despite their simple nervous systems, insects efficiently search for and find sources of odorants. Hence, it is necessary to model and implement such behavior in artificial agents (robots), to enable them to detect dangerous substances such as drugs, gas leaks, and explosives. Previous studies have approached behavioral modeling with either statistical or machine-learning methods. In this study, we determined the behavior trajectories of male silk moths using a virtual reality (VR) system. We then modeled these trajectories as a Markov decision process (MDP) and employed inverse reinforcement learning (IRL) to learn their reward function. Furthermore, we estimated the optimal policy from the learned reward function. We then conducted olfactory search simulations and determined that the IRL-based policy could locate odor sources with a high success rate. This was also investigated under environmental conditions different from those faced by real moths on the VR system. The obtained results indicate that IRL can generically represent olfactory search strategies that are adaptable to various environments.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Simulation of Spinal Muscle Control in Human Gait Using OpenSim
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  Authors: Andrii Shachykov;Julien Frère;Patrick Hénaff;
  Pages: 254 - 265
  Abstract: This paper presents a neuro-musculoskeletal simulation approach to human gait based on an original model of central pattern generator and the muscle synergy approach. The controller, shown here, aims at simplifying movement control by reducing the number of parameters to optimize. The model of the simplified motor coordination chain was built, from the midbrain through spinal neurons to the muscles allowing of simulating some neural and muscular values of gait, hard to obtain otherwise. The work also includes a bio-inspired reflex-based balance controller designed from knowledge of spinal gait. The used anatomic musculoskeletal model has been implemented in OpenSim platform. Time simulations show the ability of the platform to be able to produce different gait patterns including nominal and disturbed. Both gaits are controlled by the same bio-inspired closed-loop CPG- and reflex-based circuitries. After 2 second standing phase, the model was able to walk for about 5 meters with 10 steps. 2 steps were disrupted in the second simulation.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
TinyLFP: A Tiny Local-Field-Potential Sensor
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  Authors: Lekshmy Sudha Kumari;Abbas Z. Kouzani;
  Pages: 266 - 273
  Abstract: Efficient acquisition and analysis of neural signals play a significant role in enhancing our understanding of the brain function and its abnormalities. Neural interfaces involve electrodes inserted into the brain tissues, and also hardware circuits and software to record neural activities and analyze them. Local field potentials (LFPs) are the low frequency neural signals recorded invasively capturing the dynamic extracellular neuronal activities. The information on different bands of neural oscillations (alpha, beta, theta, and gamma) in LFP signals has been used to determine brain abnormalities. LFP signals contain components with amplitudes in the range of 10- $100 ~mu text{V}$ , which are small and can be easily distorted by external noise sources. With the advancements in closed-loop brain stimulation paradigms, there is a need for miniaturized sensors to capture and monitor LFP signals. The sensed LFP signals can then be used to close the feedback loop in a control system to adjust stimulation parameters in a closed-loop brain stimulation device. A LFP sensor requires a low power circuit to ensure prolonged operation. In addition, an area-efficient and low-noise front end stage needs to be formed. Considering these requirements, a single-channel, power-efficient, low-noise, compact neural sensor for acquisition of LFP signals is presented. The miniaturized LFP sensor, named TinyLFP, has a low-noise first stage instrumentation amplifier for providing a pre-amplification of 100 V/V, a band pass filter with a pass band of 4–200 Hz, and a post amplifier stage with an amplification factor of 200 V/V. Overall, the device provides a gain of around 85 dB for the signals in the range of 4 to 200 Hz to be used as the first stage of a closed loop optogenetic brain stimulation device.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Intuitive Environmental Perception Assistance for Blind Amputees Using
Spatial Audio Rendering
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  Authors: Xuhui Hu;Aiguo Song;Hong Zeng;Dapeng Chen;
  Pages: 274 - 284
  Abstract: Vision and touch are essential sensory systems for human to interact with the environment. For the blind amputees, how to quickly and intuitively convey the environmental information to them is one of the key issues for recovering their daily living ability. Inspired by the auditory localization ability of human, we constructed a virtual scene almost identical to reality, and concurrently added a virtual sound source to the interactive object. Leveraging the method of spatial audio rendering (SAR), the three-dimensional motion of the virtual sound source can be vividly simulated in real-time. Finally, a myoelectric prosthetic control system was developed to assist blind amputees in their daily activities, The Fitts’ law test on target localization was conducted on both SAR and voice prompt (VP) based path guidance methods, the results indicate that SAR significantly improves the information transfer rate. The results of prosthetic control test show that SAR reduces the completion time by half than the VP, while restoring the natural grasping path. With the advantage of intuitive and rich perception, the SAR demonstrated the potential applications for blind amputees to reconstruct the control and sensory loops.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Influence of Electrode Positions on Performance of Hand Motion Capture
Using EIT
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  Authors: Shunsuke Yoshimoto;Yumi Toyoda;Akio Yamamoto;
  Pages: 285 - 288
  Abstract: This paper reports the influence of electrode positions on the performance of hand motion capture conducted by using an electrical impedance tomography (EIT) device. We developed a simulator using a linear elastic and conductive model of the forearm and evaluated the sensitivity and redundancy of the EIT signals for various electrode configurations. The results of the simulation indicated that electrode positions affect the sensitivity and redundancy; specifically, the placement of grounding and sensing electrodes on the anterior side with high density has twice the sensitivity of the equally spaced placement. Furthermore, results of the subjective experiment where 1 healthy male participated matched the simulation results and suggested that the two-row electrode placement on the anterior side with high density has high sensitivity. These findings are useful for future accurate hand motion capture using an EIT device.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)
Reviewers of the IEEE Transactions on Medical Robotics and Bionics
(January–December 2021)
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  Pages: 289 - 295
  Abstract: The Editor-in-Chief and the Editors of the IEEE Transactions on Medical Robotics and Bionics gratefully acknowledge the contribution of the following reviewers who voluntarily peer reviewed manuscripts submitted for publication in our Journal during the third year of editorial activity. Their valuable time and professional expertise ensure the quality and the scientific soundness of the Journal.
  PubDate: Feb. 2022
  Issue No: Vol. 4, No. 1 (2022)