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Image-guided Breast Biopsy of MRI-visible Lesions with a Hand-mounted Motorised Needle Steering Tool

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Image-guided Breast Biopsy of MRI-visible Lesions with a Han...

International Symposium on Medical robotics (ISMR)

作者： Marta Lagomarsino Vincent Groenhuis Maura Casadio Marcel K. Welleweerd Françoise J. Siepel Stefano Stramigioli Robotics and Mechatronics (RAM) Laboratory Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands University of Genoa Genoa Italy Human-Robot Interfaces and Physical Interaction (HRII) Laboratory Istituto Italiano di Tecnologia Genoa Italy Politecnico di Milano Milan Italy

ISBN: (纸本)9781665406239

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient’s skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge.

关键词： Target tracking Biopsy Phantoms Breast Needles Real-time systems Skin

A Laser-Scribed Wearable Strain Sensing System Powered by an Integrated Rechargeable Thin-Film Zinc-Air Battery for a Long-Time Continuous Healthcare Monitoring

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SSRN 2022年

作者： Chen, Xue Hou, Zhanrui Li, Guoxian Yu, Wei Xue, Ye Niu, Guangyu Xin, Mingyang Yang, Li Meng, Chuizhou Guo, Shijie State Key Laboratory of Reliability and Intelligence of Electrical Equipment Key Laboratory of Bioelectromagnetics and Neuroengineering of Hebei Province School of Electrical Engineering Hebei University of Technology Tianjin300130 China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction School of Mechanical Engineering Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin300130 China Department of Architecture and Art Hebei University of Technology Tianjin300130 China

Wearable sensors with long cruising times without the need for an external wire-connected power supply are urgently required for a continuous physiological signal monitoring. Herein, we report the design and fabrication of a wearable all-in-one sensing system that integrates a sensitive strain sensor and a rechargeable zinc-air battery on the same laser-induced graphene (LIG) platform. The sensing electrode of the strain sensor, the catalytic air electrode of the zinc-air battery and the interconnection lines between the two are simultaneously fabricated through the same laser-scribing process on polyimide (PI) followed by pattern transfer to a flexible and stretchable polydimethylsiloxane (PDMS) substrate. In the sensor, the conductive porous LIG exhibits a high gauge factor (~27028.1), a wide detection range (~40%) and a well cyclic loading-unloading stability (>20000 cycles). In the zinc-air battery, Co3O4 nanoparticles are in-situ laser-sintered on LIG to form an excellent electrode/electrolyte/air three-phased interfacing structure for a high catalytic performance, and a moisturized tetraethyl ammonium hydroxide (TEAOH)-KOH/polyvinyl alcohol (PVA) gel electrolyte tightly binds the Co3O4 /LIG air electrode and zinc foil electrode together into an integrate thin-film energy device, which provides a high open-circuit voltage (~1.39 V) and a high specific capacity (712 mAh g-1 at 0.2 mA cm-2 ). For a proof-of-concept, an intelligent wristband based on the sensing system was fabricated and worn on the wearer’s wrist for a long-time continuous pulse monitoring of up to 10 h. The health state of the wearer can be depicted upon the acquired pulse waveforms. The developed LIG-based sensing system with a high internal powering capability will be promising in wearable sensing applications. © 2022, The Authors. All rights reserved.

关键词： Graphene

Ultrathin and Highly-Stable Rubber Electrodes Based on Island-Bridge Multi-Filler Conductive Network for Multilayer-Stacked Dielectric Elastomer Artificial Muscles

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SSRN 2024年

作者： Yu, Wei Chen, Wenqiang Yuan, Wenjing Li, Guoxian Meng, Chuizhou Guo, Shijie State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection Technology Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction School of Mechanical Engineering Hebei University of Technology Tianjin300401 China School of Materials Science & Engineering Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology Hebei University of Technology Tianjin300130 China

Dielectric elastomer actuators (DEAs) have shown great promise in applications ranging from millimeter-sized haptic feedback to centimeter-scaled soft robots. However, past progress has been focused on the soft dielectric elastomers, and little attention has been paid to the compliant electrodes. Being stable to maintain conductive while enduring repeated large deformations and compliant to avoid stiffness increasing for the dielectric elastomer remains challenging. Moreover, the fabrication of multilayer DEAs is complex, hindering the practicality. Herein, an ultrathin (~500 nm) and highly stable acrylate rubber electrode based on unique island-bridge multi-filler conductive network is developed with outstanding electrical conductivity of 2.7 S cm-1, excellent stretchability of over 1200%, and superior cycling stability of 500,000 cycles at 30% uniaxial strain. An efficient stacking method based on conductive and dielectric layer co-stretching (CDCS) approach is proposed for assembling multilayer-stacked DEAs. The 5-layer DEA reaches a large actuation strain of >17% for lifting a heavy 1-kg load and can serve as biceps muscle to drive an artificial lower arm by a maximum rotation angle of 8°, demonstrating its potential in actuating humanoid robot in an essentially soft way. This conductive rubber electrode and CDCS method are promising for developing soft robotics and muscle-like actuators. © 2024, The Authors. All rights reserved.

关键词： Electrodes

A Highly Sensitive Stretchable Constraint Fiber Mat Sensor with Tailored Near-Zero Poisson's Ratio for Very Early Stage of Battery Over Charge/Overdischarge Monitoring

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SSRN 2024年

作者： Liu, Guoqing Zhang, Yingying Li, Bo Guo, Shijie Yuan, Wenjing Meng, Chuizhou School of Materials Science & Engineering Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology Hebei University of Technology Tianjin300130 China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection Technology Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction School of Mechanical Engineering Hebei University of Technology Tianjin300401 China

Battery state monitoring is crucial for safe operation and efficient management of lithium-ion battery (LIB) systems, but early stage of timely and accurate diagnosis remains challenging. Herein, we propose to utilize a highly sensitive stretchable strain sensor to in-situ monitor the slight strain variation of cells during charge and discharge, through which the state of the battery can be accurately reflected. The high sensing capability is realized by tailoring the Poisson’s ratio of low-modulus conductive fiber mats through a facile constraint manner. The non-uniform strain distribution together with the near-zero Poisson’s ratio synergistically result in superior sensing performance, including ultralow strain detection limit (0.006%), high strain sensitivity particularly under small strains (gauge factor of 116.1 between 0–2% strain), and high strain resolution under large deformations. When being practically utilized for LIB monitoring, the constraint fiber mat sensor can produce instant resistance response reflecting the cell capacity, and offer timely warning signal once the cell is overcharged or overdischarged. The developed stretchable strain sensor possesses comprehensive advantages of low modulus, tailorable small size, easy integration and conformality to cells, and little temperature interference, which would serve as a promising candidate for future battery state analysis and safety monitoring. © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

A Valid Frequency Range Analysis of Throat Signal for Voice Command System

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A Valid Frequency Range Analysis of Throat Signal for Voice ...

International Conference on Information Science and Applications (ICISA)

作者： Young-Giu Jung Mun-Sung Han Kyung-Yong Chung Sang Jo Lee Multimodal & Human Interaction Laboratory MY-Nail Tech Incheon South Korea Robot/Cognition System Research Department Electronics and Telecommunications Research Institute Daejeon South Korea School of Computer Information Engineering Sangji University Wonju South Korea Language&Information Processing Kyungpook National University Daegu South Korea

ISBN: (纸本)9781424492220

In this paper, a valid frequency range of throat signal analyses is presented. Compare to speech signals, the throat signals have many differences in the amount of information. In this context, targeting on throat signals to find valid frequency range is one of the most important analyses for the development of new breed of feature extraction algorithm. In this respect, using analysis similarity of throat signals feature distribution and correlation analysis of throat signals in the frequency domain, the frequency range can be found with effective and quick response time to be applied to a voice command recognition system using neck-microphone. As a result, the proposed frequency range is from 200Hz (center frequency) to 2001Hz (center frequency). For the neck-microphone voice command system using the proposed frequency range, a significant reduction in time complexity of 25% can be achieved without any loss in recognition rate.

关键词： Feature extraction Microphones Correlation Speech recognition Band pass filters Finite impulse response filter Indexes

A Droplet-Based Multivolume Microfluidic Device for Digital Polymerase Chain Reaction

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SSRN 2022年

作者： Wei, Chunyang Yu, Chengzhuang Li, Shanshan Meng, Jiyu Li, Tiejun Cheng, Jingmeng Li, Junwei Hebei Key Laboratory of Robotic Sensing and Human-robot Interactions School of Mechanical Engineering Hebei University of Technology Tianjin300132 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Institute of Biophysics School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin300401 China Department of Computer Science and Electrical Engineering Hebei University of Technology Langfang065000 China

Digital PCR plays an essential role in detecting and quantifying nucleic acid targets, while there are still challenges, including reduction of cost, simplification of operations, and system miniaturization. This work proposes a droplet-based multivolume microfluidic device for easy-to-operate and integrated droplet digital PCR (ddPCR). With an elaborate " asymmetrical droplet-splitting" microstructure, we have successfully prepared a small droplet population ( n = 1.5k, Φ = 59.1 ± 2.2 μm) and a large droplet population ( n = 1.5k, Φ = 106.3 ± 3.9 μm) in the same chip for realizing multivolume design, by which the ddPCR chip achieves a dynamic range of over 10^4 for DNA molecule quantification. The chip integrates droplet generation, droplet array self-assembly, droplet dPCR, and fluorescence detection on a single device. The quantitative capability of the ddPCR chip was evaluated by measuring template DNA at concentrations from 20 to 12500 copies/μL. Benefiting from the characteristics of wide dynamic range, easy operation, and miniaturization, our ddPCR device is expected to be used in the applications involving DNA molecule quantification, including diagnosis of genetic disease, single-cell gene expression, and point of care field. © 2022, The Authors. All rights reserved.

关键词： Drops

Fully Stretchable, Porous Mxene-Graphene Foam Nanocomposites for Energy Harvesting and Self-Powered Sensing

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SSRN 2022年

作者： Yang, Li Liu, Chaosai Yuan, Wenjing Meng, Chuizhou Dutta, Ankan Chen, Xue Guo, Langang Niu, Guangyu Cheng, Huanyu State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin300130 China Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology School of Materials Science & Engineering Hebei University of Technology Tianjin300130 China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction School of Mechanical Engineering Hebei University of Technology Tianjin300401 China Department of Engineering Science and Mechanics The Pennsylvania State University University Park 16802 United States State Key Laboratory of Reliability and Intelligence of Electrical Equipment Key Laboratory of Bioelectromagnetics and Neuroengineering of Hebei Province School of Electrical Engineering Hebei University of Technology Tianjin300130 China School of Architecture and Art Hebei University of Technology Tianjin300130 China

The growing demand for intelligent wearable electronic devices has spurred the rapid developments of high-performance deformable power supplies such as triboelectric nanogenerators (TENGs) with high output performance. However, the intrinsically stretchable TENGs especially those prepared with low-cost manufacturing approaches still suffer from poor performance. To address the challenge, this paper presents a fully stretchable TENG consisting of an intrinsically stretchable MXene/silicone elastomer and silver nanowires (Ag NWs)-graphene foam nanocomposite. The intrinsically stretchable TENG exhibits high output performance (voltage, current, and power of 73.6 V, 7.75 μA, and 2.76 W m -2 ), long-term reliability, and stable electrical output under various extreme deformation conditions. In addition to the application on the human skin and clothing for human motion monitoring and detecting the strength training postures, the intrinsically stretchable TENG can also harvest the intermittent mechanical energy from human bodies to charge various energy storage units such as commercial capacitors for driving wearable electronic devices. The resulting systems have been demonstrated in applications from home anti-theft to water resources early warning systems, which provide the proof-of-the-concept demonstrations for the next-generation standalone device platforms. © 2022, The Authors. All rights reserved.

关键词： Nanogenerators

A Scalable Deep Attention Mechanism of Instance Segmentation for the Investigation of Chromosome

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SLAS technology 2025年 100306页

作者： Neelam Umbreen Sara Ali Hasan Sajid Yasar Ayaz Shrooq Alsenan Yunyoung Nam So Yeon Kim Muhammad Baber Sial National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: neelamumbreen.phd@smme.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan Suzhou Industrial Park (SIP) Monash Research Institute of Science and Technology Monash University 8Q9C+R6W Suzhou Jiangsu China. School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad NUST-COVENTRY Human-Robot Interaction (NC-HRI) Laboratory Pakistan Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: sarababer@smme.nust.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: hasan.sajid@smme.nust.edu.pk. Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: yasar@smme.nust.edu.pk. Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. Electronic address: shalsenan@pnu.edu.sa. Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang University Asan 31538 Korea. Electronic address: ynam@sch.ac.kr. Department of Artificial Intelligence Department of Software and Computer Engineering Ajou University. Electronic address: jebi1771@ajou.ac.kr. School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Electronic address: babersial@***.

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi

关键词： Chromosome segmentation Instance segmentation, cytogenetics Mask R-CNN attention mechanism biomedical imaging multi-scale feature fusion

A Hand Motion Recognition System Combining Wearable Sensing Gloves and Template Matching Algorithm

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A Hand Motion Recognition System Combining Wearable Sensing ...

IEEE International Conference on Mechatronics and Automation

作者： Dapeng Wang Yupeng Fu Bo Yang Teng Liu Chuizhou Meng Shijie Guo State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology P. R. China Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection Technology Hebei University of Technology P. R. China Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction Hebei University of Technology P. R. China School of Mechanical Engineering Hebei University of Technology P. R. China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Hand motion recognition systems, especially those these using wearable electronic glove, have received growing attention in the field of human-computer interaction and medical rehabilitation. However, two problems still exist: (1) the sensors are usually single, which fails to acquire the multi-sensor information; and (2) Pattern recognition based on deep learning algorithms tend to require a large number of samples to ensure pattern recognition accuracy, whereas obtaining a large amount of hand movement information from a patient is difficult. To address the above problems, this study develops a hand motion recognition system through combining a wearable sensing glove and a new template matching algorithm. The wearable sensing glove employs five Flex sensors and one Inertial Measurement Unit (IMU) to obtain the gesture information, followed by realization of the human-computer interaction through the Unity3D software. Meanwhile, a simple template matching algorithm is constructed for the hand gesture recognition, which can recognize 10 numbers and 20 letters in American Sign Language (ASL) with an accuracy of 90.11% and 87.13%, respectively. The proposed hand motion recognition system can basically meet the needs of patients for hand movement recognition.

关键词： human computer interaction Wrist Sign language Accuracy Software algorithms Software Sensors