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IEEE National Conference on Radar

作者： Chunzhuo Wang T. Sunil Kumar Walter De Raedt Guido Camps Hans Hallez Bart Vanrumste The e-Media Research Lab and the ESAT-STADIUS Division KU Leuven Leuven Belgium The Life Science Department IMEC Heverlee Belgium Department of Agrotechnology and Food Sciences The Division of Human Nutrition and Health Wageningen University and Research and the OnePlanet Research Center Wageningen Netherlands Department of Computer Science The M-Group DistriNet KU Leuven Sint-Michiels Belgium

Automated food intake monitoring has drawn significant attention due to its potential applications in the healthcare domain. Plenty of research, including wrist-worn imu-based and camera-based approaches, have emerged to detect food intake activity passively and objectively. Recently, researchers explored radar for food intake monitoring because of its contactless and privacy-preserving characteristics. In this study, we deploy the Frequency Modulated Continuous Wave (FMCW) radar in three different positions to investigate the performance of each position in automated eating gesture detection. The three positions are front, side, and overhead. Fifteen participants are recruited to have three meals (45 meals, 641 min in total), while the radar is deployed in different positions in each meal. A 3D Temporal Convolutional Network (3D-TCN) is used to process the range-doppler cube (RD Cube) of each dataset. The Leave-One-Subject-Out (LOSO) validation method shows that putting radar in the front position obtains the best performance with a segmental F1-score of 0.786 and 0.825 for eating and drinking gestures, respectively.

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Survey and Improvement Strategies for Gene Prioritization with Large Language Models

arXiv

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arXiv 2025年

作者： Neeley, Matthew B. Qi, Guantong Wang, Guanchu Tang, Ruixiang Mao, Dongxue Liu, Chaozhong Pasupuleti, Sasidhar Yuan, Bo Xia, Fan Liu, Pengfei Liu, Zhandong Hu, Xia Jan and Dan Duncan Neurological Research Institute Texas Children’s Hospital Houston United States Graduate School of Biomedical Sciences Program in Quantitative and Computational Biosciences Baylor College of Medicine Houston United States Graduate School of Biomedical Sciences Program in Genetics and Genomics Baylor College of Medicine Houston United States Department of Computer Science Rice University Houston United States Department of Computer Science Rutgers University NJ United States Department of Pediatrics Baylor College of Medicine Houston United States Department of Molecular and Human Genetics Baylor College of Medicine Houston United States Human Genome Sequencing Center Baylor College of Medicine Houston United States Baylor Genetics Houston United States

Background Rare diseases can be difficult to diagnose due to limited patient data and broad genetic diversity. Despite the advances in variant prioritization tools, many rare disease cases remain undiagnosed. While large language models (LLMs) have performed well in medical exams, their accuracy in diagnosing rare genetic diseases has not yet been evaluated. Methods To identify causal genes for genetic diseases, we benchmarked various LLMs for gene prioritization. We employed multi-agent and human Phenotype Ontology (HPO) classification approaches to identify patient case groups based on phenotypes categorizing them into different levels of solvability. To address LLM limitations in ranking large gene sets, we used a divide-and-conquer strategy to break down the ranking task into smaller subsets. Mini-batching inputs and limiting the number of generated tokens improved efficiency. Results In its vanilla form, GPT-4 consistently outperformed the other LLMs with an accuracy around 30%. Multi-agent and HPO classification approaches aided us in distinguishing between confidently-solved and challenging cases. In addition, we observed bias towards well-studied genes and input order sensitivity which are drawbacks of LLMs in disease-causal gene prioritization. Our divide-and-conquer strategy enhanced accuracy by overcoming positional and gene frequency biases in literature. This framework, based on benchmarking insights and novel techniques, significantly optimizes the overall process when identifying disease-causal genes for genetic diseases when compared to our baseline evaluation, and thereby better enables the development of targeted diagnostic and therapeutic interventions. Conclusions Using HPO classification and our novel multi-agent techniques, and our LLM divide-and-conquer strategy (1) highlighted the importance of accounting for differences in patient case solvability and (2) yielded improved performance in identifying causal genes for rare diseases when compare

关键词： Gene Ontology

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Intake Gesture Detection With IMU Sensor in Free-Living Environments: The Effects of Measuring Two-Hand Intake and Down-Sampling

Intake Gesture Detection With IMU Sensor in Free-Living Envi...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Chunzhuo Wang Jiaze Kong Yutong Cai T. Sunil Kumar Walter De Raedt Guido Camps Hans Hallez Bart Vanrumste The e-Media Research Lab and the ESAT-STADIUS Division KU Leuven Leuven Belgium The Life Science Department IMEC Heverlee Belgium Department of Agrotechnology and Food Sciences The Division of Human Nutrition and Health Wageningen University and Research and the OnePlanet Research Center Wageningen The Netherlands Department of Computer Science The M-Group DistriNet KU Leuven Sint-Michiels Belgium

Food intake monitoring plays an important role in personal dietary systems. Numerous approaches have been proposed to automatically detect eating gestures using various sensors and machine learning. However, existing eating gesture detection approaches mainly focus on meal sessions. Such a task is still challenging in free-living environments due to longer monitoring duration and more non-feeding activities. This paper proposes a wearable Inertial Measurement Unit (IMU) based method to detect eating and drinking gestures in free-living environments. Two important factors that impede intake gesture detection in free-living environments are addressed: 1) how to handle IMU data from two hands, and 2) what is the impact of downsampling sensor data on performance. To integrate two-hand data, we propose a solution that combines hand mirroring and temporal concatenation techniques. The multi-stage temporal convolutional network (MS-TCN) is applied to effectively recognise intake gestures. A dataset contains 12 subjects with 67.5 h data is collected for validation. Moreover, IMU data with different sampling frequencies are processed to test performance. Validated by Leave-One-Subject-Out (LOSO) method, our approach (with 16 Hz sampling frequency) achieves a segmental F1-score of 0.826 and 0.893 for recognizing eating and drinking gestures, respectively. Results show that the proposed solution outperforms existing two-hand data combination approaches. Moreover, in our case, a higher sampling frequency does not always mean better performance.

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Image-based Gait Spatiotemporal Parameters Estimation using a Single Camera and CNN-Transformer Hybrid Network

Image-based Gait Spatiotemporal Parameters Estimation using ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ankhzaya Jamsrandorj Quynh Hoang Ngan Nguyen Dawoon Jung Min Seok Baek Kyung-Ryoul Mun Jinwook Kim Department of Human Computer Interface & Robotics Engineering KIST School University of Science & Technology Seoul Republic of Korea Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Republic of Korea Department of AI Robotics KIST School University of Science & Technology Seoul Republic of Korea Department of Neurology Wonju Severance Christian Hospital Yonsei University Wonju College of Medicine Wonju Republic of Korea

Vision-based gait analysis can play an important role in the remote and continuous monitoring of the elderly’s health conditions. However, most vision-based approaches compute gait spatiotemporal parameters using human pose information and provide average parameters. This study aimed to propose a straightforward method for stride-by-stride gait spatiotemporal parameters estimation. A total of 160 elderly individuals participated in this study. Data were gathered with a GAITRite system and a mobile camera simultaneously. Three deep learning networks were trained with a few RGB frames as input and a continuous 1D signal containing both spatial and temporal gait parameters as output. The trained networks estimated the stride lengths with correlations of 0.938 and more and detected gait events with F 1 -scores of 0.914 and *** relevance— The proposed method showed excellent agreements with the GAITRite system in analyzing spatiotemporal gait parameters. Our approach can be applied to monitor the elderly’s health conditions based on their gait parameters for early diagnosis of diseases, proper treatment, and timely intervention.

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Explaining the Black-box Smoothly - A Counterfactual Approach

arXiv

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arXiv 2021年

作者： Singla, Sumedha Eslami, Motahhare Pollack, Brian Wallace, Stephen Batmanghelich, Kayhan Computer Science Department The University of Pittsburgh PittsburghPA15206 United States School of Computer Science Human-Computer Interaction Institute Carnegie Mellon University United States Department of Biomedical Informatics The University of Pittsburgh PittsburghPA15206 United States University of Pittsburgh Medical School PittsburghPA15206 United States

We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., saliency maps) that assess feature importance do not explain how imaging features in important anatomical regions are relevant to the classification decision. Such reasoning is crucial for transparent decision-making in healthcare applications. Our framework explains the decision for a target class by gradually exaggerating the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation. Our proposed loss function preserves essential details (e.g., support devices) in the generated images. We used counterfactual explanations from our framework to audit a classifier trained on a chest x-ray dataset with multiple labels. Clinical evaluation of model explanations is a challenging task. We proposed clinically-relevant quantitative metrics such as cardiothoracic ratio and the score of a healthy costophrenic recess to evaluate our explanations. We used these metrics to quantify the counterfactual changes between the populations with negative and positive decisions for a diagnosis by the given classifier. We conducted a human-grounded experiment with diagnostic radiology residents to compare different styles of explanations (no explanation, saliency map, cycleGAN explanation, and our counterfactual explanation) by evaluating different aspects of explanations: (1) understandability, (2) classifier's decision justification, (3) visual quality, (d) identity preservation, and (5) overall helpfulness of an explanation to the users. Our results show that our counterfactual explanation was the only explanation method that significantly improved the users' understanding of the classifier's decision compared to the no-explanation baseline. Our metric

关键词： Medical applications

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Two Faces of Privacy: Legal and human-centered Perspectives of Lifelogging Applications in Home Environments 6th

Two Faces of Privacy: Legal and Human-Centered Perspectives ...

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6th International Conference on human Aspects of IT for the Aged Population, ITAP 2020, held as part of the 22nd International Conference on human-computer interaction, HCII 2020

作者： Wilkowska, Wiktoria Offermann-van Heek, Julia Colonna, Liane Ziefle, Martina Human-Computer Interaction Center RWTH Aachen University Campus-Boulevard 57 Aachen52074 Germany Department of Law Stockholm University Universitetvägen 10C Stockholm10691 Sweden

ISBN: (纸本)9783030502485

In view of the consequences resulting from the demographic change, using assisting lifelogging technologies in domestic environments represents one potential approach to support elderly and people in need of care to stay longer within their own home. Yet, the handling of personal data poses a considerable challenge to the perceptions of privacy and data security, and therefore for an accepted use in this regard. The present study focuses on aspects of data management in the context of two different lifelogging applications, considering a legal and a human-centered perspective. In a two-step empirical process, consisting of qualitative interviews and an online survey, these aspects were explored and evaluated by a representative German sample of adult participants (N = 209). Findings show positive attitudes towards using lifelogging, but there are also high requirements on privacy and data security as well as anonymization of the data. In addition, the study allows deep insights into preferred duration and location of the data storage, and permissions to access the personal information from third parties. Knowledge of preferences and requirements in the area of data management from the legal and human-centered perspectives is crucial for lifelogging and must be considered in applications that support people in their daily living at home. Outcomes of the present study considerably contribute to the understanding of an optimal infrastructure of the accepted and willingly utilized lifelogging applications. © 2020, Springer Nature Switzerland AG.

关键词： Information management

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Investigating the interaction Experience with Augmented Reality Based Writing Tools 21st

Investigating the Interaction Experience with Augmented Real...

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21st International Conference on human-computer interaction, HCI International 2019

作者： Zheng, Wei Yi, Xin Shi, Yuanchun Pervasive Human-Computer Interaction Group Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9783030235277

The contents of this paper include the design methodology of perceptual computing products, the innovative design of the AR writing and its technical feasibility, the writing interactive experience test of different hardware platforms, etc. It has certain reference value for the research work of paperless writing. © Springer Nature Switzerland AG 2019.

关键词： Augmented reality

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Evoking Stress Reactivity in a Virtual Dance Competition

Evoking Stress Reactivity in a Virtual Dance Competition

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AHFE Virtual Conference on human Factors and Simulation and the International Conference on Digital human Modeling and Applied Optimization, 2020

作者： van Dammen, Lotte Barnett, Neil Conrady, Roselynn Wright, Lucas Thymes, Bradon Shirtcliff, Elizabeth A. Department of Human Development and Family Studies Iowa State University AmesIA United States Virtual Reality Application Center Iowa State University AmesIA United States Department of Mechanical Engineering San Diego State University San DiegoCA United States Hamilton College Physics Department Hamilton ClintonNY United States Computer Science Department Hamilton ClintonNY United States Electrical Engineering and Computer Science Department Howard University WashingtonDC United States

ISBN: (纸本)9783030510633

Stress reactivity involves a physiological response to a stressful task, as a biological mechanism related to mental and physical health outcomes. Traditional stressors evoke social evaluative threat, which is fear of judgment and a threat to social status, typically via public speaking tasks. To be well-suited for virtual reality, however, a novel approach is necessary in the design of new stress tasks. We hypothesized that a virtual reality stress task involving social evaluative threat through a dance competition in front of an audience, elicits a stress response, measured by autonomic nervous system, cortisol, and testosterone reactivity. Participants (n = 18) showed autonomic nervous system reactivity in terms of increased heart rate and decreased respiratory sinus arrhythmia, indicating a stress response. Levels of cortisol increased in response to the dance competition, especially within responders, whereas testosterone levels did not change significantly over time. A virtual reality dance competition involving physical social evaluative threat elicits a stress response. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtual reality

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop

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SCHIZOPHRENIA BULLETIN 2023年第SUPP2期49卷 S86-S92页

作者： Corona Hernandez, Hugo Corcoran, Cheryl Achim, Amelie M. de Boer, Janna N. Boerma, Tessel Brederoo, Sanne G. Cecchi, Guillermo A. Ciampelli, Silvia Elvevag, Brita Fusaroli, Riccardo Giordano, Silvia Hauglid, Mathias van Hessen, Arjan Hinzen, Wolfram Homan, Philipp de Kloet, Sybren F. Koops, Sanne Kuperberg, Gina R. Maheshwari, Kritika Mota, Natalia B. Parola, Alberto Rocca, Roberta Sommer, Iris E. C. Truong, Khiet Voppel, Alban E. van Vugt, Marieke Wijnen, Frank Palaniyappan, Lena Department of Biomedical Sciences of Cells and Systems University Medical Center Groningen University of Groningen Groningen Netherlands Department of Psychiatry Icahn School of Medicine at Mount Sinai NY USA Département de Psychiatrie et Neurosciences VITAM Centre de Recherche en Santé Durable Cervo Brain Research Centre Université Laval Québec Canada Department of Psychiatry University Medical Center Utrecht Utrecht University Utrecht Netherlands Department of Languages Literature and Communication Institute for Language Sciences Utrecht University Utrecht Netherlands University Center of Psychiatry University Medical Center Groningen Groningen Netherlands IBM T. J. Watson Research Center Yorktown Heights NY USA Department of Clinical Medicine University of Tromsø—the Arctic University of Norway Tromsø Norway Department of Linguistics Cognitive Science and Semiotics School of Communication and Culture Aarhus University Aarhus Denmark Department of Culture Interacting Minds Center Cognition and Computation Communication School of Culture and Society Aarhus University Aarhus Denmark Linguistic Data Consortium University of Pennsylvania PA USA Department of Innovative Technologies University of Applied Sciences and Arts of Southern Switzerland Lugano Switzerland Faculty of Law University of Tromsø—the Arctic University of Norway Tromsø Norway Department of Human Media Interaction University of Twente Enschede Netherlands Institució Catalana de Recerca i Estudis Avançats (ICREA) Universitat Pompeu Fabra Barcelona Spain Department of Psychiatry Psychiatric Hospital of the University of Zurich Psychotherapy and Psychosomatics Zurich Switzerland Neurocast BV Amsterdam Netherlands Department of Psychology Tufts University Medford MA USA Department of Psychiatry Harvard Medical School Boston MA USA The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Genetics U

This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.

关键词： speech technology implementation digital markers psychiatric practice pathophysiology

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