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Closed-looped sensing and stimulation system for Parkinson's disease early diagnosis and rehabilitation

Smart Health 2022年 26卷

作者： Cai, Yi Qian, Xiaoye Li, Qin Lin, Feng Huang, Ming-Chun Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland OH United States Department of Data and Computational Science Duke Kunshan University Jiangsu 215316 China ZJU-Hangzhou Global Scientific and Technological Innovation Center School of Cyber Science and Technology Zhejiang University Zhejiang 310058 China Suzhou Huanmu Intelligence Technology Co. Ltd. Jiangsu China

Parkinson's disease (PD) patients are involved in motor dysfunctions and gait issues. The absence of long-term reliable gait rehabilitations could result in poor gait function, gait deficits, and locomotion problems. Meanwhile, PD patients usually face significant challenges to complete the rehabilitation programs effectively without a specialized gait laboratory or after discharge from the hospital. This paper presents a closed-loop sensing and computing system to facilitate long-term medical care for PD patients. The proposed system consists of wearable sensing, data streaming, online data processing, real-time auditory stimulation for PD gait rehabilitation, and data services for medical providers. To address the potential issues of user-friendly and comforts, we implemented a wrapped shoe based plantar pressure sensing device for under-feet pressure data collection. Afterward, quantitative measurements of gait are fulfilled through online data streaming and processing architecture. Physical therapy is generally preferred for PD rehabilitation because it provides a more significant benefit superior to the drugs. Hence, applications of gait activity recognition and closed-loop stimulation are proposed to build a reliable closed-loop system and support self-contained training. To evaluate the proposed system, we conduct experiments with three gait and PD related datasets. It broads Parkinson's gait health care prospects that make long-term PD rehabilitation possible in community-living environment. © 2022 Elsevier Inc.

关键词： data streaming processing Gait rehabilitation Rhythmic auditory stimulation Wearable sensing

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Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning

arXiv

引用

arXiv 2022年

作者： Jang, Se-In Girard, Michaël J.A. Thiery, Alexandre H. Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School Boston United States Center for Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School BostonMA United States Ophthalmic Engineering and Innovation Laboratory Singapore Eye Research Institute Singapore Singapore Duke-NUS Medical School Singapore Singapore Institute for Molecular and Clinical Ophthalmology Basel Switzerland Department of Statistics and Data Science National University of Singapore Singapore Singapore

In this paper, we propose an explainable and interpretable diabetic retinopathy (ExplainDR) classification model based on neural-symbolic learning. To gain explainability, a highlevel symbolic representation should be considered in decision making. Specifically, we introduce a human-readable symbolic representation, which follows a taxonomy style of diabetic retinopathy characteristics related to eye health conditions to achieve explainability. We then include humanreadable features obtained from the symbolic representation in the disease prediction. Experimental results on a diabetic retinopathy classification dataset show that our proposed ExplainDR method exhibits promising performance when compared to that from state-of-the-art methods applied to the IDRiD dataset, while also providing interpretability and explainability. © 2022, CC BY.

关键词： Eye protection

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Optimal half-metal band structure for large thermoelectric performance

arXiv

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arXiv 2023年

作者： Rahangiar, Finantius E.M. Cahaya, Adam B. Muntini, Melania S. Anshori, Isa Hasdeo, Eddwi H. Department of Physics Universitas Gadjah Mada Sekip Utara BLS 21 Yogyakarta55281 Indonesia Research Center for Quantum Physics National Research and Innovation Agency South Tangerang Indonesia Department of Physics Faculty of Mathematics and Natural Sciences University of Indonesia Depok16424 Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology West Java Bandung Indonesia Bandung Institute of Technology West Java Bandung Indonesia Department of Physics and Material Science University of Luxembourg LuxembourgL-1511 Luxembourg

Half-metal ferromagnets were predicted [in IEEE Trans. Mag. 51, 1 (2015)] to give large thermoelectric performance in anti-parallel spin valve configuration. Despite being metals that suffer from the Wiedemann-Franz law, the additional spin degrees of freedom allow for tuning of the thermoelectric properties due to the spin-valve enhancement factor (SVEF). We test this theory and find a mismatch of parameters that gives large TE performance and large SVEF. As a result, we show that the spin-valve setup is useful only for gapless HMF with initially poor TE performance. To obtain the largest TE performance, one still needs to open the band gap. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy gap

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In Memory of Leslie G. Ungerleider

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Neuroscience Bulletin 2021年第4期37卷 592-595页

作者： Ning Liu Hui Zhang Xilin Zhang Jiongjiong Yang Xuchu Weng Lin Chen State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing100101China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing100191China Key Laboratory of Brain Cognition and Education SciencesMinistry of EducationSouth China Normal UniversityGuangzhou510631GuangdongChina School of Psychology South China Normal UniversityGuangzhou510631GuangdongChina School of Psychological and Cognitive Sciences Peking UniversityBeijing100871China Institute for Brain Research and Rehabilitation South China Normal UniversityGuangzhou510631GuangdongChina University of Chinese Academy of Sciences Beijing100049China

Dr. Leslie Ungerleider, who together with Mortimer Mishkin established the model of ‘‘two functionally dissociated cortical visual pathways in the primate brain’’[1], passed away on December 11, 2020. Ungerleider... 详细信息

关键词： visual timer eider

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Utilizing Wearable GRF and EMG Sensing System and Machine Learning Algorithms to Enable Locomotion Mode Recognition for In-home Rehabilitation

Utilizing Wearable GRF and EMG Sensing System and Machine Le...

引用

IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)

作者： Chaoming Fang Yixuan Wang Shuo Gao School of Instrumentation Science and Optoelectronic Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Interdisciplinary Innovation Institute of Medicine and Engineering Beihang University Beijing China

The following topics are dealt with: polymers; printing; electrochemical electrodes; flexible electronics; capacitive sensors; patient monitoring; nanofabrication; silver; polymer films; textiles.

ISBN: (数字)9781728152783

ISBN: (纸本)9781728152790

The following topics are dealt with: polymers; printing; electrochemical electrodes; flexible electronics; capacitive sensors; patient monitoring; nanofabrication; silver; polymer films; textiles.

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Concurrent plantar stress sensing and energy harvesting technique by piezoelectric insole device and rectifying circuitry for gait monitoring in the internet of health things

TechRxiv

引用

TechRxiv 2020年

作者： Kang, Shuaibo Lin, Jingjing Chen, Junliang Dai, Yanning Wang, Zhiheng Gao, Shuo School of Instrumentation Science and Optoelectronic Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Interdisciplinary Innovation Institute of Medicine and Engineering Beihang University Beijing China

Concurrent high force detection accuracy and extended battery lifetime are strongly expected in wearable gait monitoring systems, which are important for many Internet of Health Things (IoHT) applications. In this article, a piezoelectric insole device and rectifying circuitry based technique is presented to achieve these two ultimate goals. Here, walking induced positive and negative charges are separated for plantar stress detection and energy harvesting respectively, realizing the two functions concurrently. Experimental results demonstrate that first, the high detection sensitivity of 55 mN and responsivity of 231 mV/N are achieved, satisfying the need for diagnosing various diseases;second, energy of 1.6 pJ is stored during a walking event, consequently extending the battery lifetime. The developed technique enhances the development of gait monitoring in IoHT. © 2020, CC BY.

关键词： Energy harvesting

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ACE-HGNN: Adaptive curvature exploration hyperbolic graph neural network

arXiv

引用

arXiv 2021年

作者： Fu, Xingcheng Li, Jianxin Wu, Jia Sun, Qingyun Ji, Cheng Wang, Senzhang Tan, Jiajun Peng, Hao Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Department of Computing Macquarie University Sydney Australia School of Computer Science and Engineering Central South University Changsha410083 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existing GNNs embed graph data into Euclidean space and thus are less effective to capture the ubiquitous hierarchical structures in real-world networks. Hyperbolic Graph Neural Networks (HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning. In hyperbolic geometry, the graph hierarchical structure can be reflected by the curvatures of the hyperbolic space, and different curvatures can model different hierarchical structures of a graph. However, most existing HGNNs manually set the curvature to a fixed value for simplicity, which achieves a suboptimal performance of graph learning due to the complex and diverse hierarchical structures of the graphs. To resolve this problem, we propose an Adaptive Curvature Exploration Hyperbolic Graph Neural Network named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Specifically, ACE-HGNN exploits a multi-agent reinforcement learning framework and contains two agents, ACE-Agent and HGNN-Agent for learning the curvature and node representations, respectively. The two agents are updated by a Nash Q-leaning algorithm collaboratively, seeking the optimal hyperbolic space indexed by the curvature. Extensive experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Flexible and wearable GRF and EMG sensors enabled locomotion mode recognition for IoHT based in-home rehabilitation

TechRxiv

引用

TechRxiv 2020年

作者： Fang, Chaoming Wang, Yixuan Gao, Shuo School of Instrumentation Science and Optoelectronic Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Interdisciplinary Innovation Institute of Medicine and Engineering Beihang University Beijing China

Benefiting from the development of the Internet of Healthcare Things (IoHT) in recent years, locomotion mode recognition using wearable sensors plays an important role in the field of in-home rehabilitation. In this paper, a smart sensing system utilizing flexible electromyography (EMG) sensors and ground reaction force (GRF) sensors for locomotion mode recognition is presented, together with its use under the IoHT architecture. EMG and GRF information from ten healthy subjects in five common locomotion modes in daily life were collected, analyzed, and then transmitted to remote end terminals (e.g., personal computers). The data analysis process was implemented with machine learning techniques (Support Vector Machine), through which the locomotion modes were determined with a high accuracy of 96.38%. This article demonstrates a feasible means for accurate locomotion mode recognition by combining wearable sensing techniques and the machine learning algorithm, potentially advancing the development for IoHT based in-home rehabilitation. © 2020, CC BY.

关键词： Wearable sensors

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Concurrent Plantar Stress Sensing and Energy Harvesting Technique by Piezoelectric Insole Device and Rectifying Circuitry for Gait Monitoring in the Internet of Health Things

Concurrent Plantar Stress Sensing and Energy Harvesting Tech...

引用

IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)

作者： Shuaibo Kang Jingjing Lin Junliang Chen Yanning Dai Zhiheng Wang Shuo Gao School of Instrumentation Science and Optoelectronic Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Interdisciplinary Innovation Institute of Medicine and Engineering Beihang University Beijing China

ISBN: (数字)9781728152783

ISBN: (纸本)9781728152790

Concurrent high force detection accuracy and extended battery lifetime are strongly expected in wearable gait monitoring systems, which are important for many Internet of Health Things (IoHT) applications. In this article, a piezoelectric insole device and rectifying circuitry based technique is presented to achieve these two ultimate goals. Here, walking induced positive and negative charges are separated for plantar stress detection and energy harvesting respectively, realizing the two functions concurrently. Experimental results demonstrate that first, the high detection sensitivity of 55 mN and responsivity of 231 mV/N are achieved, satisfying the need for diagnosing various diseases; second, energy of 1.6 pJ is stored during a walking event, consequently extending the battery lifetime. The developed technique enhances the development of gait monitoring in IoHT.

关键词： Legged locomotion Sensors Internet Batteries Energy harvesting Monitoring Stress

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A Quasi-1D Potential for Bose Gas Phase Fluctuations

引用

Chinese Physics Letters 2019年第8期36卷 10-13页

作者： Shi-Feng Yang Zi-Tong Xu Kai Wang Xiu-Fei Li Yue-Yang Zhai Xu-Zong Chen School of Electronics Engineering and Computer Science Peking UniversityBeijing100871 Science and Technology on Inertial Laboratory Beihang UniversityBeijing100191 Beijing Academy of Quantum Information Sciences Beijing100193 Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beijing100191

An elongated trap potential for cold atoms is designed based on a quadrupole-Ioffe *** fluctuations in a Bose-Einstein condensate(BEC),which is confined by the trap,are *** simulate the atom density distribution induced by fluctuation after time of flight from this elongated trap potential and study the temperature measurement method related to the ***,taking advantage of the tight confinement and radio frequency dressing technique,we propose a double well potential for splitting *** results are helpful for improving understanding of low-dimensional quantum gases and provide important guidance for atomic interferometry.

关键词： Bose Gas Phase Fluctuations

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