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Portable Multiparameter biomedical Sensor Fusion for Home Healthcare 2

Portable Multiparameter Biomedical Sensor Fusion for Home He...

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Vidyasagar, K.E.Ch Manchikanti, Venkata Jaideep Datta Kanduri, Harika Saikia, Manob Jyoti University of Memphis Biomedical Sensors and Systems Lab MemphisTN38152 United States Osmania University Department of Biomedical Engineering Hyderabad500007 India University of Memphis Electrical and Computer Engineering Department MemphisTN38152 United States

ISBN: (纸本)9798350366570

The ever-increasing focus on preventative healthcare and remote patient monitoring has driven the need for innovative solutions. Traditionally, measuring vital signs in both clinical and home settings has relied on a collection of individual devices, often leading to cluttered setups, patient discomfort, and potential inaccuracies due to user error. This paper proposes a multi-parameter acquisition system designed to address these limitations. The system integrates the measurement of essential parameters into a single, user-friendly device. This streamlined approach not only reduces measurement complexity but also improves patient comfort by eliminating the need for multiple devices and cumbersome wires. The design of the system, including its sensors, hardware components, and data processing unit is presented in detail. The device serves a dual purpose. Firstly, it empowers individuals at home by providing them with a single device to monitor their vital signs. Secondly, for hospitals, the device offers a streamlined and less cumbersome way to visualize essential patient parameters, allowing for improved monitoring efficiency. © 2024 IEEE.

关键词： Remote patient monitoring

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Change of electrical and Transport Properties of Nickel Oxide by Carrier Concentration and Temperature through First‑Principle Calculations

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Nanomanufacturing and Metrology 2023年第4期6卷 109-118页

作者： Nayem Md.Reza Shah Chang‑Dong Yeo Minyeong Choi Yang‑Ki Hong Jeong H.You Department of Mechanical Engineering Texas Tech UniversityLubbockTX 79409USA Department of Electrical and Computer Engineering and Material Science Ph.D.Program The University of AlabamaTuscaloosaAL 35487USA Department of Mechanical Engineering School of EngineeringUniversity of St.ThomasSt.PaulMN 55105USA

Nickel is typically used as one of the main components in electrical contact devices or *** oxide(NiO)is usually formed on the surfaces of electrodes and can negatively impact system performance by introducing electrical contact *** thermal,electrical,and transport properties of NiO,as a Mott insulator or a p-type semiconductor,can be altered by operating and environmental conditions such as temperature and stress/strain by *** this study,we inves-tigate the fundamental material properties of NiO through the first-principle ***,we obtain and compare the lattice parameter,magnetic moment,and electronic structure for NiO via the WIEN2K simulations with four different poten-tials(i.e.,GGA,GGA+U,LSDA,and LSDA+U).Then,using the WIEN2K simulation results with LSDA+U potential that produces a highly accurate bandgap for NiO,we calculate the electrical conductivity and electrical part of the thermal conductivity of nickel and NiO as a function of temperature and carrier concentration through the BoltzTraP *** simulation results revealed that the electrical conductivity relative to the relaxation time for NiO increases with the carrier concentration,while it shows a slightly decreasing trend with temperature under a fixed carrier *** contrast,the electrical part of the thermal conductivity shows an increasing trend considering carrier concentration and temperature.

关键词： Nickel oxide Magnetic moments Density of states Seebeck coefcient electrical conductivity

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A Multimodality Electrochemical and Impedance Spectroscopy System-on-a-Chip With Temperature Sensing and Impedance-Boosting Techniques

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IEEE Transactions on biomedical Circuits and Systems 2023年第4期17卷 857-871页

作者： Lin, Yi-Jie Liu, Wei-Cheng Huang, Yu-Chiao Huang, Ying-Jie Yeh, Yung-Hua Chang, Min-Hua Lin, Shu-Ping Liao, Ying-Chih Liao, Yu-Te National Yang Ming Chiao Tung University Institute of Electrical and Computer Engineering Hsinchu300 Taiwan National Chung Hsing University Graduate Institute of Biomedical Engineering Taichung40227 Taiwan National Taiwan University Department of Chemical Engineering Taipei10617 Taiwan

This article presents a multimodal electrochemical sensing system-on-chip (SoC), including the functions of cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and temperature sensing. CV readout circuitry achieves an adaptive readout current range of 145.5 dB through an automatic range adjustment and resolution scaling technique. EIS has an impedance resolution of 9.2 mΩ Hz at a sweep frequency of 10 kHz and an output current of up to 120 μA. With an impedance boost mechanism, the maximum detectable load impedance is extended to 22.95 kΩ, while the total harmonic distortion is less than 1%. A resistor-based temperature sensor using a swing-boosted relaxation oscillator can achieve a resolution of 31 mK in 0-85 °C. The design is implemented in a 0.18 μm CMOS process. The total power consumption is 1 mW. © 2007-2012 IEEE.

关键词： Temperature sensors

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Gamified Rehabilitation and Physiotherapy Using MediaPipe for Posture and Hand Gesture Recognition 2

Gamified Rehabilitation and Physiotherapy Using MediaPipe fo...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Vidyasagar, K.E. Ch Sai, G. N. K. Anantha Ruchita, Munagala Saikia, Manob Jyoti University of Memphis Biomedical Sensors and Systems Lab MemphisTN38152 United States Osmania University Department of Biomedical Engineering Hyderabad500007 India University of Memphis Electrical and Computer Engineering Department MemphisTN38152 United States

ISBN: (纸本)9798350366570

Introduction: Rehabilitation for conditions affecting mobility, such as stroke, muscle atrophy, and others, poses significant challenges in traditional physiotherapy practices. Engaging patients in enjoyable exercises is crucial for maintaining compliance with rehabilitation protocols. In response, this paper introduces a pioneering game-based approach to rehabilitation, leveraging posture detection technology. Materials and Methods: A game-based rehabilitation system was developed utilizing deep learning techniques through the MediaPipe framework for posture and hand gesture recognition. The system analyzes user posture in real time via a webcam and translates these movements into game controls, allowing patients to interact with popular games like Subway Surfers to simulate physical activities such as running and jumping. Latency between patient movements and in-game responses was measured across three hardware configurations (8GB, 12GB, and 16GB RAM) to evaluate system performance and responsiveness. Results and Discussion: Latency decreased with higher RAM, with the lowest measured at 430 ms on a 16GB configuration. This real-time responsiveness enhances patient engagement, making the rehabilitation process more immersive and effective. By leveraging posture detection technology, this innovative system bridges the gap between the necessity of rehabilitation exercises and the appeal of gaming, offering a promising solution for improving treatment outcomes across diverse age groups and conditions. © 2024 IEEE.

关键词： Gesture recognition

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Measuring and modeling the effects of vagus nerve stimulation on heart rate and laryngeal muscles

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Bioelectronic Medicine 2023年第1期9卷 1-26页

作者： Huffman, William J. Musselman, Eric D. Pelot, Nicole A. Grill, Warren M. Department of Biomedical Engineering Duke University Fitzpatrick CIEMAS Box 90281 Room 1427 101 Science Drive DurhamNC27708-0281 United States Department of Electrical and Computer Engineering Duke University Durham United States Department of Neurobiology Engineering Duke University Durham United States Department of Neurosurgery Engineering Duke University Durham United States

Background: Reduced heart rate (HR) during vagus nerve stimulation (VNS) is associated with therapy for heart failure, but stimulation frequency and amplitude are limited by patient tolerance. An understanding of physiological responses to parameter adjustments would allow differential control of therapeutic and side effects. To investigate selective modulation of the physiological responses to VNS, we quantified the effects and interactions of parameter selection on two physiological outcomes: one related to therapy (reduced HR) and one related to side effects (laryngeal muscle EMG). Methods: We applied a broad range of stimulation parameters (mean pulse rates (MPR), intra-burst frequencies, and amplitudes) to the vagus nerve of anesthetized mice. We leveraged the in vivo recordings to parameterize and validate computational models of HR and laryngeal muscle activity across amplitudes and temporal patterns of VNS. We constructed a finite element model of excitation of fibers within the mouse cervical vagus nerve. Results: HR decreased with increased amplitude, increased MPR, and decreased intra-burst frequency. EMG increased with increased MPR. Preferential HR effects over laryngeal EMG effects required combined adjustments of amplitude and MPR. The model of HR responses highlighted contributions of ganglionic filtering to VNS-evoked changes in HR at high stimulation frequencies. Overlap in activation thresholds between small and large modeled fibers was consistent with the overlap in dynamic ranges of related physiological measures (HR and EMG). Conclusion: The present study provides insights into physiological responses to VNS required for informed parameter adjustment to modulate selectively therapeutic effects and side effects. © 2023, The Author(s).

关键词： Muscle

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SpikeNAS-Bench: Benchmarking NAS Algorithms for Spiking Neural Network Architecture

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1614-1625页

作者： Sun, Genchen Liu, Zhengkun Gan, Lin Su, Hang Li, Ting Zhao, Wenfeng Sun, Biao Tianjin University School of Electrical and Information Engineering Tianjin300072 China Northwestern Polytechnical University School of Automation Xi'an710072 China Politecnico di Milano Department of Electronics Information and Bioengineering Milan20133 Italy Chinese Academy of Medical Science Institute of Biomedical Engineering Pecking Union Medical College Tianjin300020 China Binghamton University Department of Electrical and Computer Engineering State University of New York BinghamtonNY United States

In recent years, Neural Architecture Search (NAS) has marked significant advancements, yet its efficacy is marred by the dependence on substantial computational resources. To mitigate this, the development of NAS benchmarks has emerged, offering datasets that enumerate all potential network architectures and their performances within a predefined search space. Nonetheless, these benchmarks predominantly focus on convolutional architectures, which are criticized for their limited interpretability and suboptimal hardware efficiency. Recognizing the untapped potential of Spiking Neural Networks (SNNs) - often hailed as the third generation of neural networks for their biological realism and computational thrift - this study introduces SpikeNAS-Bench. As a pioneering benchmark for SNN, SpikeNAS-Bench utilizes a cell-based search space, integrating leaky integrate-and-fire (LIF) neurons with variable thresholds as candidate operations. It encompasses 15,625 candidate architectures, rigorously evaluated on CIFAR10, CIFAR100 and Tiny-ImageNet datasets. This paper delves into the architectural nuances of SpikeNAS-Bench, leveraging various criteria to underscore the benchmark's utility and presenting insights that could steer future NAS algorithm designs. Moreover, we assess the benchmark's consistency through three distinct proxy types: zero-cost-based, early-stop-based, and predictor-based proxies. Additionally, the paper benchmarks seven contemporary NAS algorithms to attest to SpikeNAS-Bench's broad applicability. We commit to providing training logs, diagnostic data for all candidate architectures, and the promise to release all code and datasets post-acceptance, aiming to catalyze further exploration and innovation within the SNN domain. SpikeNAS-Bench is open source at https://***/XXX (hidden for double anonymous review). © 2020 IEEE.

关键词： HTTP

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Quizzing the Quizzes: Improving Cancer Risk Factor Analysis Quizzes 24

Quizzing the Quizzes: Improving Cancer Risk Factor Analysis ...

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24th International Conference on Control Systems and computer Science, CSCS 2023

作者： Vlach, Jared Dascalu, Sergiu Shen, Yantao Park, Jeongwon University of Nevada Reno Department of Biomedical Engineering Reno United States University of Nevada Reno Department of Computer Science and Engineering Reno United States University of Nevada Reno Department of Electrical and Biomedical Engineering Reno United States

ISBN: (纸本)9798350313390

This user study addresses the characteristics of breast cancer risk factor analysis quizzes in order to understand the correlation between completion time, question clarity, and user satisfaction. The user study was conducted with 10 participants to understand what users find preferable in their overall experience with cancer risk factor analysis tools. Three risk factor analysis tools (one of them created by us) were compared in terms of completion time, completion time per question, questions asked to the coordinator, and preferred tool and/or features preferred. Results showed that shorter completion time is not necessarily preferred by users but understanding and clarity of questions enabled users to complete the questionnaires with less confusion. However, results were inconclusive regarding completion time per question as the tools had similar results and, perhaps surprisingly, the most preferred tool was the one with the longest average completion time per question. © 2023 IEEE.

关键词： Factor analysis

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Quantifying Deep Learning Model Uncertainty in Conformal Prediction

Quantifying Deep Learning Model Uncertainty in Conformal Pre...

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2023 AAAI Summer Symposium Series, SuSS 2023

作者： Karimi, Hamed Samavi, Reza Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada Vector Institute TorontoON Canada

Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decisionmaking in machine learning and statistical modeling, particularly in the context of medical AI. Conformal Prediction (CP) has emerged as a promising framework for representing the model uncertainty by providing well-calibrated confidence levels for individual predictions. However, the quantification of model uncertainty in conformal prediction remains an active research area, yet to be fully addressed. In this paper, we explore state-of-the-art CP methodologies and their theoretical foundations. We propose a probabilistic approach in quantifying the model uncertainty derived from the produced prediction sets in conformal prediction and provide certified boundaries for the computed uncertainty. By doing so, we allow model uncertainty measured by CP to be compared by other uncertainty quantification methods such as Bayesian (e.g., MC-Dropout and DeepEnsemble) and Evidential approaches. © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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A Robust Finite Time Longitudinal Speed Control for a Catenary-based Electric Vehicle 63

A Robust Finite Time Longitudinal Speed Control for a Catena...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rinaldi, Gianmario Ferrara, Antonella Menon, Prathyush P University of Exeter Faculty of Environment Science and Economy Department of Engineering EX4 4QF United Kingdom University of Pavia Computer and Biomedical Engineering Department of Electrical Pavia Italy

ISBN: (纸本)9798350316339

The electrification of the transportation sector plays a crucial role in reducing the global carbon footprint. Among the various solutions, catenary-based electric vehicles have attracted a great deal of interest from researchers and practitioners. This article considers a vehicle powered by a battery energy storage system (BESS) and a catenary system and proposes a robust finite-time longitudinal speed control strategy. Drawing inspiration from the Second-Order Sliding Mode (2-SOSM) methodology, the approach ensures that the control input remains within the limits set by the maximum torque of the motor. The strategy demonstrates robustness against uncertainties and disturbances, eliminating the need to know the model parameters. The simulation case study considers the effects of real road data, including slope variations, on power management. A comparative analysis with the conventional Proportional Integral (PI) method highlights the superior performance of the proposed strategy. © 2024 IEEE.

关键词： Carbon footprint

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Au/SiC Microfluidic Devices Fabricated by Rapid Laser Cladding for Photocatalytic Degradation of Water Pollutants

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ACS Sustainable Chemistry and engineering 2024年第11期12卷 4486-4496页

作者： Chang, Chun-Hao Lin, Hsiu-Pen Chen, Chien-Wei Lu, Yu-Cheng Hsiao, Fu-He Hsieh, Chi-Hsiang Lin, Bo-Cheng Yu, Bang-Ying Ma, Dai-Liang Kuo, Hao-Chung Tu, Chang-Ching Li, Bor-Ran Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Centre for Emergent Functional Matter Science National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrophysics College of Science National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Taisic Materials Corp. Taoyuan320 Taiwan Department of Electrical Engineering National Central University Taoyuan32001 Taiwan Medical Device Innovation and Translation R&D Center National Yang Ming Chiao Tung University Taipei112304 Taiwan

The increasing proliferation of third-generation semiconductor silicon carbide (SiC) products has led to the emergence of SiC powder as an industrial byproduct. To reutilize the SiC efficiently, we propose a facile method for fabrication of photocatalytic plates from reclaimed SiC. Our approach involves affixing the SiC onto a glass surface with laser cladding, followed by sputter deposition of a gold film and laser annealing. The resulting nanogold-coated SiC plate exhibited enhanced photocatalytic activity due to the injection of hot electrons into the SiC via surface plasmon resonance. Unlike chemical reduction, which requires hours for the synthesis, purification, and isolation of Au/SiC, our approach accomplished this in just 30 min. When illuminated, the Au/SiC plate generated electron-hole pairs that formed radicals in the presence of water and oxygen, leading to the decomposition of methylene blue. Furthermore, the Au/SiC plate can be integrated into a microfluidic device for an enhanced degradation rate and efficiency. This research provides a simple and environmentally friendly approach to the production of photocatalytic plates from reclaimed SiC and contributes to sustainable development. © 2024 The Authors. Published by American Chemical Society.

关键词： Silicon carbide

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