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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Liam M. Sullivan Suzi Creveling Marissa Cowan Lukas Gabert Tommaso Lenzi Department of Mechanical Engineering and Robotics Center University of Utah Rocky Mountain Center for Occupational and Environmental Health Department of Biomedical Engineering University of Utah

Ambulation in everyday life requires walking at variable speeds, variable inclines, and variable terrains. Powered prostheses aim to provide this adaptability through control of the actuated joints. Some powered prosthesis controllers can adapt to discrete changes in speed and incline but require manual tuning to determine the control parameters, leading to poor clinical viability. Other data-driven controllers can continuously adapt to changes in speed and incline but do so by imposing the same non-amputee gait patterns for all amputee subjects, which does not consider subjective preferences and differing clinical needs of users. Here, we present a controller for powered knee and ankle prostheses that can continuously adapt to different walking speeds, inclines, and uneven terrains without enforcing a specific prosthesis position, impedance, or torque. A virtual biarticular muscle connection determines the knee flexion torque, which changes with both speed and slope. Adaptation to inclines and uneven terrains is based solely on the global shank orientation. Continuously variable damping allows for speed adaptation. Minimum-jerk programming defines the prosthesis swing trajectory at variable cadences. Experiments with one individual with an above-knee amputation suggest that the proposed controller can effectively adapt to different walking speeds, inclines, and rough terrains.

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Design and Evaluation of a Powered Hip Exoskeleton for Frontal and Sagittal Plane Assistance

Design and Evaluation of a Powered Hip Exoskeleton for Front...

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IEEE/RAS-EMBS International Conference on biomedical robotics and Biomechatronics (BioRob)

作者： Dante Archangeli Brendon Ortolano Rosemarie Murray Lukas Gabert Tommaso Lenzi Department of Mechanical Engineering and the Robotics Center University of Utah Salt Lake City UT USA Rocky Mountain Center for Occupational and Environmental Health Department of Biomedical Engineering University of Utah

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Powered exoskeletons have the potential to improve ambulation for millions of individuals who struggle with mobility. Most powered exoskeletons aim to improve walking economy and increase speed by generating propulsive torque in the sagittal plane. However, individuals with mobility impairments typically have limited mediolateral balance, which requires assistance in the frontal plane. Here we present the design and preliminary evaluation of an autonomous powered hip exoskeleton that can generate torque in both the frontal and sagittal planes. The exoskeleton leverages a unique parallel actuator to produce up to 30 Nm of torque while achieving a compact and lightweight design that adds only 3 cm posterior and 8 cm lateral to the user and weighs only 5.3 kg. Preliminary validation tests with two healthy subjects show that the proposed powered hip exoskeleton can successfully assist gait by controlling the frontal plane torque to alter step width and providing sagittal plane torque to assist with hip flexion. A device with these characteristics has the potential to improve both gait economy and balance in clinical populations.

关键词： Legged locomotion Actuators Torque Exoskeletons Wearable robots Kinematics Biomechatronics Hip Testing

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A Unified Controller for Natural Ambulation on Stairs and Level Ground with a Powered Robotic Knee Prosthesis

A Unified Controller for Natural Ambulation on Stairs and Le...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Marissa Cowan Suzi Creveling Liam M. Sullivan Lukas Gabert Tommaso Lenzi Department of Mechanical Engineering and the Robotics Center at the University of Utah Rocky Mountain Center for Occupational and Environmental Health Department of Biomedical Engineering at the University of Utah

Powered lower-limb prostheses have the potential to improve amputee mobility by closely imitating the biomechanical function of the missing biological leg. To accomplish this goal, powered prostheses need controllers that can seamlessly adapt to the ambulation activity intended by the user. Most powered prosthesis control architectures address this issue by switching between specific controllers for each activity. This approach requires online classification of the intended ambulation activity. Unfortunately, any misclassification can cause the prosthesis to perform a different movement than the user expects, increasing the likelihood of falls and injuries. Therefore, classification approaches require near-perfect accuracy to be used safely in real life. In this paper, we propose a unified controller for powered knee prostheses which allows for walking, stair ascent, and stair descent without the need for explicit activity classification. Experiments with one individual with an above-knee amputation show that the proposed controller enables seamless transitions between activities. Moreover, transition between activities is possible while leading with either the sound-side or the prosthesis. A controller with these characteristics has the potential to improve amputee mobility.

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A Review of Deep Learning and Machine Learning Approaches in COVID-19 Detection 2

A Review of Deep Learning and Machine Learning Approaches in...

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2nd IEEE International Conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Abubakar, Hassana Al-Turjman, Fadi Ameen, Zubaida Said Mubarak, Auwalu Saleh Faculty of Engineering Near East University Biomedical Engineering Department Mersin10 Turkey Research Center for Ai and IoT Ai and Robotics Institute Near East University Ai Engineering Department Mersin10 Turkey

ISBN: (纸本)9798350334104

COVID-19 resulted from SARS-CoV-2 and has disastrous effects on human. It has obliged researchers worldwide to exploit approaches for an accurate and reliable diagnostic tools for it early detection. However, the pandemic nature of this disease has made the traditional diagnostic methods ineffective. Artificial Intelligence (AI) researchers have come up with a number of promising algorithms to attain most effective and rapid classification system that can alternate the tedious and time consuming traditional diagnostic techniques. These algorithms used either Computed Tomography (CT) images, X-ray images or both for COVID-19 classification. The most recent exploited Deep Learning (DL) and Machine Learning (ML) approaches along with feature extraction techniques mostly on CT images are reviewed in this paper. The overall accuracy of these techniques ranged from 86.1% to 99.7% which indicate that they are applicable in COVID-19 detection. This paper will assist researchers in future development of these techniques for COVID-19 diagnosis. © 2022 IEEE.

关键词： COVID-19

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Nonlinearity of optoacoustic signals and a new contrast mechanism for imaging

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光：科学与应用（英文版） 2025年第5期14卷 1420-1432页

作者： Jaber Malekzadeh-Najafabadi Jaya Prakash Daniel Razansky Jorge Ripoll Vipul Gujrati Vasilis Ntziachristos Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Institute for Biomedical Engineering Department of Information Technology and Electrical EngineeringETH ZurichZurichSwitzerland Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Department of Instrumentation and Applied Physics Indian Institute of ScienceBengaluruIndia Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology Faculty of MedicineUniversity of ZurichZurichSwitzerland Institute for Biomedical Engineering Department of Information Technology and Electrical EngineeringETH ZurichZurichSwitzerland Department of Bioengineering and Aerospace Engineering Universidad Carlos Ⅲ de MadridMadridSpain Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Biological Imaging Central Institute for Translational Cancer Research(TranslaTUM)School of Medicine and Health & School of ComputationInformation and TechnologyTechnical University of MunichMunichGermany Institute of Biological and Medical Imaging Bioengineering CenterHelmholtz Zentrum MünchenNeuherbergGermany Munich Institute of Robotics and Machine Intelligence(MIRMI) Technical University of MunichMunichGermany

Optoacoustic signals behave nonlinearly at light fluences above a few mJ/cm2,which may affect the interpretation and quantification of *** has been proposed that optoacoustic nonlinearity arises from the heat-induced formation of nanobubbles or changes in local thermo-physical ***,such explanations are only valid at much higher fluences than typically used in biomedical optoacoustic imaging(＞20 mJ/cm2)or in the presence of materials with high absorption coefficients such as gold *** propose herein that electromagnetic permittivity changes in response to photon absorption are major source of optoacoustic signal nonlinearity at low *** provide theoretical and experimental evidence that supports this postulation and show that optoacoustic pressure responses due to permittivity changes,which are function of thermally excited third-order nonlinear susceptibility,can explain the nonlinear behavior of the optoacoustic *** different materials exhibit different thermally excited third-order nonlinear susceptibility,this property could function as a new contrast mechanism that can identify the sensitivity of a substance's dielectric constant to photon-induced temperature ***,we propose an imaging method based on nonlinear optoacoustic signals that exploits this newly identified contrast *** findings may have far-reaching implications for improving the accuracy of optoacoustics and utilizing the proposed new contrast mechanism would advance our understanding of cellular and tissue functionality.

关键词： mechanism contrast imaging nonlinearity optoacoustic signals

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Lumbar spine localisation method based on feature fusion

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 931-945页

作者： Yonghong Zhang Ning Hu Zhuofu Li Xuquan Ji Shanshan Liu Youyang Sha Xiongkang Song Jian Zhang Lei Hu Weishi Li Robotics Institute School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina Beijing Zhuzheng Robot Co. LTDBeijingChina Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKnoxvilleTennesseeUSA Department of Orthopaedics Peking University Third HospitalBeijingChina Engineering Research Center of Bone and Joint Precision Medicine Ministry of EducationBeijingChina Beijing Key Laboratory of Spinal Disease Research BeijingChina School of Biological Science and Medical Engineering Beihang UniversityBeijingChina Department of Computer Science University of WarwickCoventryUK

To eliminate unnecessary background information,such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning,a two‐stage approach for localising lumbar segments is ***,based on the multi‐scale feature fusion technology,a non‐linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine,effectively eliminating useless background information,such as soft *** the second stage,we directly realised the precise positioning of each segment in the lumbar spine space region based on the non‐linear regression method,thus effectively eliminating the interference caused by the adjacent *** 3D Intersection over Union(3D_IOU)is used as the main evaluation indicator for the positioning *** an open dataset,3D_IOU values of 0.8339�0.0990 and 0.8559�0.0332 in the first and second stages,respectively is *** addition,the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s ***,the proposed method performs very well in terms of both pre-cision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.

关键词： CT image lumbar spatial orientation multi‐scale information fusion

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HapticFormers: Utilizing Transformers for Avocado Maturity Grading through Vision-based Tactile Assessment

HapticFormers: Utilizing Transformers for Avocado Maturity G...

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International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (HAPTICS)

作者： Israa Fahmy Taimur Hassan Irfan Hussain Naoufel Werghi Lakmal Seneviratne Khalifa University Center for Robotics and Autonomous Systems (KUCARS) Khalifa University UAE Department of Electrical Computer and Biomedical Engineering Abu Dhabi University UAE

Detecting maturity of fruits and vegetables, especially avocados, is a critical task in modern agriculture and supply chain management. Moreover, the accurate assessment of maturity can improve the harvesting time and ensure consistent quality for consumers through the supply chain process. A key approach to achieving this is the non-destructive estimation of produce quality. Vision-Based Tactile Sensing (VBTS) technologies, which mimic human tactile perception, offer a novel approach to address this challenge. This paper focuses on the use of two notable VBTS technologies, GelSight and Facebook’s DIGIT sensor. Using these technologies, we developed two novel datasets that assess the avocado maturity using the transformer models, marking a novel contribution in this area. We adapted several transformer architectures to the task, conducting experiments on both image classification and regression to estimate avocado firmness. Among the variants tested, the PoolFormer displayed notable results with accuracy of 92% in detecting avocado maturity level when used with tactile data. The datasets and code used in this study will be shared at this URL.

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A Root Extension to the Propagator Algorithm for Direction of Arrival Estimation

arXiv

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arXiv 2025年

作者： Dheerendra, Pradeep Derzsi, Zoltan Biomedical Engineering Division James Watt School of Engineering University of Glasgow Glasgow United Kingdom Center for Artificial Intelligence and Robotics New York University Abu Dhabi United Arab Emirates

In this paper, we propose an extension to the Propagator algorithm for source bearing estimation by performing root decomposition which eliminates the need for spectral search over angles. Further the propagator spatial spectrum is reused after performing the polynomial rooting to alleviate the observed drawback of an increase in RMSE at lower SNRs for estimating the directions-of-arrival of signals impinging on a linear array of sensors. Our proposed algorithm achieves a 98% reduction in computational complexity compared to Propagator alongside an improved angular resolution suitable for real time DOA estimation for wireless communications. © 2025, CC BY.

关键词： Signal to noise ratio

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Early Failure Detection in Autonomous Surgical Soft-Tissue Manipulation via Uncertainty Quantification

arXiv

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arXiv 2025年

作者： Thompson, Jordan Koe, Ronald Le, Anthony Goodman, Gabriella Brown, Daniel S. Kuntz, Alan Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States Department of Biomedical Engineering University of Utah United States

Autonomous surgical robots are a promising solution to the increasing demand for surgery amid a shortage of surgeons. Recent work has proposed learning-based approaches for the autonomous manipulation of soft tissue. However, due to variability in tissue geometries and stiffnesses, these methods do not always perform optimally, especially in out-of-distribution settings. We propose, develop, and test the first application of uncertainty quantification to learned surgical soft-tissue manipulation policies as an early identification system for task failures. We analyze two different methods of uncertainty quantification, deep ensembles and Monte Carlo dropout, and find that deep ensembles provide a stronger signal of future task success or failure. We validate our approach using the physical daVinci Research Kit (dVRK) surgical robot to perform physical soft-tissue manipulation. We show that we are able to successfully detect task failure and request human intervention when necessary while still enabling autonomous manipulation when possible. Our learned tissue manipulation policy with uncertainty-based early failure detection achieves a zero-shot sim2real performance improvement of 47.5% over the prior state of the art in learned soft-tissue manipulation. We also show that our method generalizes well to new types of tissue as well as to a bimanual soft-tissue manipulation task. Copyright © 2025, The Authors. All rights reserved.

关键词： Robotic surgery

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Automatic Classification Normal ECGs Based on Normal PathECG and WaveECG Features 50

Automatic Classification Normal ECGs Based on Normal PathECG...

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50th Computing in Cardiology, CinC 2023

作者： Pociask, Elzbieta Malinowski, Krzysztof P Mortada, Mhd Jafar Proniewska, Klaudia K Van Dam, Peter M Agh University of Krakow Department of Biocybernetics and Biomedical Engineering Poland Center for Digital Medicine and Robotics Jagiellonian University Medical College Krakow Poland Jagiellonian University Medical College Department of Bioinformatics and Telemedicine Krakow Poland Universita Politecnica Delle Marche Department of Information Engineering Ancona60121 Italy University Medical Center Utrecht Department of Cardiology Netherlands

ISBN: (纸本)9798350382525

Classification of the ECG waveform to normal or abnormal is important to the non-experienced ECG-reader. We propose an algorithm to use solely the waveform of a single ECG beat to classify the ECG as normal or abnormal. In this study we used a subset of the normal classified ECGs from the PTB-XL database to create a normal distribution of the ECG waveform (WaveECG) and its PathECG positions. The aim of this study was to use these distributions to classify all human validated ECGs from the PTB-XL database as either normal or abnormal. Our initial results show an accuracy of 87% to determine whether an ECG is normal or abnormal, irrespective of the gender group used. Using solely the ECG waveform can detect the vast majority of abnormal ECGs, including conduction disorders, ischemia, and arrhythmias. © 2023 CinC.

关键词： Electrocardiograms

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