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A technique for mimicking soft tissue manipulation from experimental data to a wave equation model for a new laparoscopic virtual reality training system

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Open biomedical engineering Journal 2021年 15卷 16-28页

作者： Nillahoot, Nantida Patel, Sneha Suthakorn, Jackrit Department of Biomedical Engineering Center for Biomedical and Robotics Technology (BART LAB) Faculty of Engineering Mahidol University Salaya Nakorn Pathom 73170 Thailand

Background: The difficulty of laparoscopic procedures and the specific psychomotor skills required support the need for a training system for intensive and repetitive practice to acquire the specific skills. The present VR training systems have some limitations with respect to the soft tissue models in the training system. This is associated with the need for a real-time simulation, which requires a balance between computational cost and accuracy. Objective: The primary objective of the study is to develop a two dimensional wave equation model that closely mimics the soft tissue manipulation in a laparoscopic procedure for a VR training system. Methods: A novel mathematical model based on the wave equation is prepared to represent the interaction between the laparoscopic tool and the soft tissue. The parameters within the model are determined through experimental analysis of a soft tissue phantom. The experimental setup involves a linear actuator applying force to the soft tissue phantom to generate deformation. Data acquisition is conducted through a camera and a robotic force acquisition system which measures force, displacement of the linear actuator and records a video. Through image processing, the displacements of the markers on the phantom’s x-y plane during its deformation are determined and these parameters are used to develop the model, which finally is validated through a comparative analysis. Results: The results from the developed model are observed and compared statistically as well as graphically with the finite element model based on deformation data. The results show that the deformation data between the developed model and the available model is significantly similar. Conclusion: This study demonstrates the adaptability of the wave equation to meet the needs of the specific surgical procedure through modification of the model based on the experimental data. Moreover, the comparative analysis further corroborates the relevance and validity of the mo

关键词： Image Processing Soft Tissue Modeling Surgical Training Virtual Reality Training System Visualization Wave Equation

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Revised in-depth meta-analysis on the efficacy of robot-assisted versus traditional free-hand pedicle screw insertion

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Laparoscopic, Endoscopic and Robotic Surgery 2024年第4期7卷 155-165页

作者： Sorayouth Chumnanvej Branesh M.Pillai Jackrit Suthakorn Siriluk Chumnanvej Neurosurgery Division Department of SurgeryFaculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand Center for Biomedical and Robotics Technology(BART LAB) Faculty of EngineeringMahidol UniversityBangkokThailand Department of Anesthesiology and Operating Room Phramongkutklao HospitalBangkokThailand

Objective:Robot-assisted pedicle screw insertion has recently emerged as an alternative to the traditional free-hand ***,discrepancies in the accuracy of screw placement between the 2 methods have been highlighted by some comparative *** meta-analysis was conducted to synthesize evidence comparing these ***:Searches were conducted in 5 electronic databases adhering to specific eligibility criteria for randomized and observational *** data were analyzed using RevMan software and the results are presented as odds ratios(ORs),mean differences,or standard mean differences(SMDs)with 95%confidence intervals(CIs).Our analysis included 12 studies(7 randomized trials and 5 observational studies,involving 883 patients and 4903 screws).Results:The results demonstrated a higher rate of Grade A Gertzbein and Robbins pedicle placement score(OR:1.77;95%CI:1.10e2.87),a lower rate of revision surgeries(OR:0.21;95%CI:0.09e0.52),and a shorter radiation exposure duration(SMD?1.38,95%CI:2.32 to0.44)in the robot-assisted group compared with the free-hand ***,the length of hospital stay,volume of intraoperative blood loss,postoperative visual analogue scale scores for back pain,and rate of wound infection were similar between the 2 *** heterogeneity was observed in some ***:Compared with the free-hand method,the robot-assisted technique provides greater accuracy and reduced radiation *** efficacy of the robot-assisted technique is expected to improve further as experience with its use in surgery grows.

关键词： Robotic surgery Pedicle screw placement Free-hand Fluoroscopy Meta-analysis

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Fluence Compensation for Linear Array-Based Photoacoustic Imaging System Using Geometrical Depth Mapping

Fluence Compensation for Linear Array-Based Photoacoustic Im...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Tang, Yichuan Lesniak, Wojciech G. Pomper, Martin G. Zhang, Haichong K. Johns Hopkins University Department of Radiology and Radiological Science BaltimoreMD United States Worcester Polytechnic Institute Department of Robotics Engineering WorcesterMA United States Worcester Polytechnic Institute Department of Biomedical Engineering WorcesterMA United States

ISBN: (纸本)9798350346459

Linear array-based photoacoustic (PA) imaging has been applied in tissue imaging. Due to optical attenuation, deep tissue receives less optical energy compared to surface tissue and thus generates weaker acoustic signals. To compensate for fluence change along light penetration direction, methods have been proposed based on optical transport model or statistical learning. However, these methods either require knowledge about optical properties of the tissue or involve extensive computation. In this work, we propose a fluence compensation method based on geometrical depth mapping derived from outer contour of the tissue. Our method requires no information on optical properties and does not involve extensive computation. Validations were carried out in simulation and ex vivo experiment. Results show that image intensities in deep tissue are enhanced, and image intensity distribution is more homogenous along the light penetration depth after fluence compensation. The proposed method proved effective for fluence compensation in imaging ex vivo tissue with homogeneous optical properties. © 2023 IEEE.

关键词： Tissue

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Soft Robot Kinematic Control Via Manipulability-Aware Redundancy Resolution

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Journal of Mechanisms and robotics 2025年第7期17卷 071013页

作者： Azizkhani, Milad Ha, Junhyoung Gunderman, Anthony L. Chen, Yue Department of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332 Center for Healthcare Robotics Artificial Intelligence and Robotics Institute Korea Institute of Science and Technology Seoul 02792 South Korea Department of Mechanical Engineering University of Arkansas Fayetteville AR 72701 Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA 30332

This article addresses the kinematic control of a redundant soft robotic arm. Full pose kinematic control of soft robots is challenging because direct application of the classical controllers developed based on rigid robots to soft robots could lead to unreliable or infeasible motions. In this study, we explore the manipulability property of a soft robotic arm and develop an advanced resolved-rate controller that prioritizes position over orientation control and switches its modes and gains based on position and orientation manipulabilities, enabling stable motion even when the robot is close to the singular configurations. The simulation and experimental results indicate that our proposed method outperforms previous methods in terms of both accuracy and smoothness during operation.

关键词： Resolution (Optics) Kinematics bio-inspired Simulation Errors Redundancy (engineering) continuum robotics Robots soft robotics Soft robots robotics kinematics Algorithms

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Towards experimental insight into magnetic flux distribution within the air gap of permanent magnet synchronous drive

Towards experimental insight into magnetic flux distribution...

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International Conference on Methods and Models in Automation and robotics (MMAR)

作者： Hubert Milanowski Adam Krzysztof Piłat Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics AGH University of Krakow Krakow Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

This report explores the experimental identification of magnetic flux density within the motor air gap, focusing on the development of a robot-based approach to automate the scanning procedure. Emphasis is placed on analyzing the distribution of magnetic flux within the motor’s spatial air gap, as well as the amplification of harmonics resulting from changes in air gap orientation. Drawing upon experimental findings, a model is proposed to illustrate the three-dimensional distribution of magnetic flux within the gap.

关键词： Solid modeling Magnetic flux density Automation Atmospheric modeling Air gaps Rotors Drives

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A Multimodal Soft Gripper with Variable Stiffness and Variable Gripping Range Based on MASH Actuator

arXiv

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arXiv 2024年

作者： Li, Dannuo Zhou, Xuanyi Xiong, Quan Yeow, Chen-Hua The Department of Biomedical Engineering and Advanced Robotics Centre National University of Singapore Singapore117583 Singapore The Department of Biomedical Engineering National University of Singapore Singapore117583 Singapore The Department of Biomedical Engineering and Advanced Robotics Centre National University of Singapore Singapore117583 Singapore The Department of Biomedical Engineering and Advanced Robotics Centre National University of Singapore Singapore117583 Singapore Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology 02139 United States

Soft pneumatic actuators with integrated strain-limiting layers have emerged as predominant components in the field of soft gripper technology for several decades. However, owing to their intrinsic strain-limiting layer design, these soft grippers possess a singular gripping functionality, rendering them incapable of adapting to diverse gripping tasks with different strategies. Based on our previous work, we introduce a novel soft gripper that offers variable stiffness, an adjustable gripping range, and multifunctionality. The MASH actuator-based soft gripper can expand its gripping range up to threefold compared to the original configuration and ensures secure grip by enhancing stiffness when handling heavy objects. Moreover, it supports multitasking gripping through specific gripping strategy control. Copyright © 2024, The Authors. All rights reserved.

关键词： Pneumatic actuators

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Suppression of thermal transport in bent boron arsenide nanoribbons

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Physical Review Applied 2024年第5期22卷 054019页

作者： Guanbo Jiao Shuo Qiao Jun Lyu Yi Tao Lin Yang Department of Advanced Manufacturing and Robotics College of Engineering School of Mechanical Engineering and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments National Key Laboratory of Advanced Micro and Nano Manufacture Technology

Boron arsenide (BAs) has emerged as a strong contender for next-generation functional materials, boasting desirable attributes such as high thermal conductivity and ambipolar mobility. However, a critical yet underexplored challenge lies in understanding its heat conduction capabilities under inhomogeneous strain, which is pivotal across various functional devices and operational conditions. Here, through modeling thermal transport in bent BAs and Si nanoribbons, a striking difference is revealed. Namely, the BAs nanoribbons exhibit nearly twice the reduction in thermal conductivity compared with Si under identical bending conditions. This significant disparity is driven by two key factors: the pronounced effect of phonon-spectra broadening under strain and the inhomogeneous strain-induced narrowing of the acoustic and optical (ao) gap. Together, these factors relax the originally restricted phonon scattering phase space and fundamentally alter phonon scattering dynamics. Our findings not only offer a new perspective on how heat transfer can be dynamically modulated in BAs, but also provide crucial insights into enhancing thermal performance in BAs-based functional devices.

关键词： Anharmonic lattice dynamics Bending Heat transfer Phonons Strain Thermal conductivity Nanoribbon Molecular dynamics

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Multi-class Classification and Pretrained Sustainable Agriculture Model for Plant Leaf Disease Detection Using Resnet-101 2

Multi-class Classification and Pretrained Sustainable Agricu...

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2nd International Conference on Computer Vision and Internet of Things, ICCVIoT 2024

作者： Teja, Ashwala Ravi Stephen, S. Subathra, M.S.P. Ewards, S.E. Vinodh Mahmood, Md. Rashid Mohan, Ashwala George, S. Thomas Department of CSE Karunya Institute of Technology and Sciences Tamil Nadu India Department of Robotics Engineering Karunya Institute of Technology and Sciences Tamil Nadu India Department of Biomedical Engineering Karunya Institute of Technology and Sciences Tamil Nadu India Department of ECE Guru Nanak Institutions Technical Campus Hyderabad India

ISBN: (纸本)9781837242672

In recent years, advancements in technology have greatly benefited the agricultural sector. One critical aspect of ensuring healthy plant growth is promptly identifying infected leaves. For sustainable agriculture, automatically detecting diseased on plant leaves before the infection spreads is highly advantageous. This paper introduces deep learning models like CNN, along with various optimization techniques and hyperparameter tuning being used. The study utilizes machine learning frameworks and a deep learning library. It encompasses 10 different datasets comprising 151,007 leaf images depicting 270 different diseases, including healthy leaves from 44 plant species. Additionally, pretrained models such as AlexNet, ResNet50, GoogLeNet, DenseNet-121, and ResNet101 are employed, with the ResNet model achieving the highest accuracy rate of 94.45%. © The Institution of engineering & Technology 2024.

关键词： Plant diseases

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An explainable machine learning approach to aid MDS-UPDRS Part III diagnosis using kinematic data of Parkinsonian gait

An explainable machine learning approach to aid MDS-UPDRS Pa...

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IISE Annual Conference and Expo 2024

作者： Moon, Jungyoon Park, Kijung Park, Kiwon Kim, Ryul Department of Industrial and Management Engineering Incheon National University Incheon22012 Korea Republic of Department of Biomedical & Robotics Engineering Incheon National University Incheon22012 Korea Republic of Department of Neurology SMG-SNU Boramae Medical Center Seoul National University College of Medicine Seoul07061 Korea Republic of

ISBN: (纸本)9781713877851

The Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III is widely used to examine the overall motor severity of patients with Parkinson's disease (PD). However, manual implementation of the MDS-UPDRS scheme for PD diagnosis demands substantial time and effort, which imposes physical and cognitive burdens on both patients and neurologists during PD examinations. To tackle this challenge, this study proposes an explainable machine learning approach to automate PD diagnosis by predicting the total MDS-UPDRS Part III score based on a patient’s kinematic data during gait. First, MDS-UPDRS Part III examinations for 30 patients with early PD symptoms were performed by a neurologist. Next, gait experiments using a motion capture system were conducted to obtain the ankle, knee, and hip joint angles of the patients at each gait cycle. The collected joint angle data were transformed into joint angle matrices for gait cycles to represent gait pattern images in lower extremities. Then, a two-dimensional convolutional neural network (2D-CNN) model was trained using the gait pattern image to estimate the total MDS-UPDRS Part III score. In addition, the gradient-weighted class activation mapping (Grad-CAM) method was employed to extract plausible gait characteristics for the obtained MDS-UPDRS score. The proposed approach not only suitably predicts the total MDS-UPDRS Part III score but also reveals that significant features for predicting the MDS-UPDRS score are linked to both the single leg support phase and the swing phase of Parkinsonian gait. © IISE Annual Conference and Expo *** rights reserved.

关键词： Convolutional neural networks

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Balancing memorization and generalization in RNNs for high performance brain-machine interfaces 23

Balancing memorization and generalization in RNNs for high p...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Joseph T. Costello Hisham Temmar Luis H. Cubillos Matthew J. Mender Dylan M. Wallace Matthew S. Willsey Parag G. Patil Cynthia A. Chestek Departments of Electrical and Computer Engineering Biomedical Engineering Robotics and Neurosurgery University of Michigan Ann Arbor MI

Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are currently limited by the accuracy of real-time decoding algorithms. Recurrent neural networks (RNNs) using modern training techniques have shown promise in accurately predicting movements from neural signals but have yet to be rigorously evaluated against other decoding algorithms in a closed-loop setting. Here we compared RNNs to other neural network architectures in real-time, continuous decoding of finger movements using intracortical signals from nonhuman primates. Across one and two finger online tasks, LSTMs (a type of RNN) outperformed convolutional and transformer-based neural networks, averaging 18% higher throughput than the convolution network. On simplified tasks with a reduced movement set, RNN decoders were allowed to memorize movement patterns and matched able-bodied control. Performance gradually dropped as the number of distinct movements increased but did not go below fully continuous decoder performance. Finally, in a two-finger task where one degree-of-freedom had poor input signals, we recovered functional control using RNNs trained to act both like a movement classifier and continuous decoder. Our results suggest that RNNs can enable functional real-time BMI control by learning and generating accurate movement patterns.

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