Smart Health最新文献_第2页

Breath as a biomarker: A survey of contact and contactless applications and approaches in respiratory monitoring 呼吸作为一种生物标志物：在呼吸监测中的接触和非接触应用和方法的调查

Smart Health Pub Date : 2025-04-16 DOI: 10.1016/j.smhl.2025.100579

Almustapha A. Wakili, Babajide J. Asaju, Woosub Jung

{"title":"Breath as a biomarker: A survey of contact and contactless applications and approaches in respiratory monitoring","authors":"Almustapha A. Wakili, Babajide J. Asaju, Woosub Jung","doi":"10.1016/j.smhl.2025.100579","DOIUrl":"10.1016/j.smhl.2025.100579","url":null,"abstract":"<div><div>Breath analysis has emerged as a critical tool in health monitoring, offering insights into respiratory function, disease detection, and continuous health assessment. While traditional contact-based methods are reliable, they often pose challenges in comfort and practicality, particularly for long-term monitoring. This survey comprehensively examines contact-based and contactless approaches, emphasizing recent advances in machine learning and deep learning techniques applied to breath analysis. Contactless methods, including Wi-Fi Channel State Information and acoustic sensing, are analyzed for their ability to provide accurate, noninvasive respiratory monitoring.</div><div>We explore a broad range of applications, from single-user respiratory rate detection to multi-user scenarios, user identification, and respiratory disease detection. Furthermore, this survey details essential data preprocessing, feature extraction, and classification techniques, offering comparative insights into machine learning/deep learning models suited to each approach. Key challenges like dataset scarcity, multi-user interference, and data privacy are also discussed, along with emerging trends like Explainable AI, federated learning, transfer learning, and hybrid modeling. By synthesizing current methodologies and identifying open research directions, this survey offers a comprehensive framework to guide future innovations in breath analysis, bridging advanced technological capabilities with practical healthcare applications.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100579"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Thermal vision: Pioneering non-invasive temperature tracking in congested spaces 热视觉：在拥挤的空间开创性的非侵入性温度跟踪

Smart Health Pub Date : 2025-04-03 DOI: 10.1016/j.smhl.2025.100576

Arijit Samal, Haroon R. Lone

{"title":"Thermal vision: Pioneering non-invasive temperature tracking in congested spaces","authors":"Arijit Samal, Haroon R. Lone","doi":"10.1016/j.smhl.2025.100576","DOIUrl":"10.1016/j.smhl.2025.100576","url":null,"abstract":"<div><div>Non-invasive temperature monitoring of individuals plays a crucial role in identifying and isolating symptomatic individuals. Temperature monitoring becomes particularly vital in settings characterized by close human proximity, often referred to as <em>dense settings</em>. However, existing research on non-invasive temperature estimation using thermal cameras has predominantly focused on <em>sparse settings</em>. Unfortunately, the risk of disease transmission is significantly higher in dense settings like movie theaters or classrooms. Consequently, there is an urgent need to develop robust temperature estimation methods tailored explicitly for dense settings.</div><div>Our study proposes a non-invasive temperature estimation system that combines a thermal camera with an edge device. Our system employs YOLO models for face detection and utilizes a regression framework for temperature estimation. We evaluated the system on a diverse dataset collected in dense and sparse settings. Our proposed face detection model achieves an impressive mAP score of over 94 in both in-dataset and cross-dataset evaluations. Furthermore, the regression framework demonstrates remarkable performance with a mean square error of 0.18 °C and an impressive <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> score of 0.96. Our experiments’ results highlight the developed system’s effectiveness, positioning it as a promising solution for continuous temperature monitoring in real-world applications. With this paper, we release our dataset and programming code publicly.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100576"},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metamorphic Testing for Robustness and Fairness Evaluation of LLM-based Automated ICD Coding Applications 基于llm的自动化ICD编码应用鲁棒性和公平性评估的变质测试

Smart Health Pub Date : 2025-04-02 DOI: 10.1016/j.smhl.2025.100564

Guna Sekaran Jaganathan, Indika Kahanda, Upulee Kanewala

{"title":"Metamorphic Testing for Robustness and Fairness Evaluation of LLM-based Automated ICD Coding Applications","authors":"Guna Sekaran Jaganathan, Indika Kahanda, Upulee Kanewala","doi":"10.1016/j.smhl.2025.100564","DOIUrl":"10.1016/j.smhl.2025.100564","url":null,"abstract":"<div><div>Healthcare and medical domain-specific LLMs (BioMed LLMs), such as PubMedBERT and Med-PaLM, are developed and pre-trained on biomedical and clinical text to be used specifically in healthcare and medical applications. The recent popularity of these BioMed LLMs increased the use of LLMs in health and medical applications to perform various critical tasks, including ICD (International Classification of Diseases) coding. For such safety-critical applications, it is vital to focus not just on accuracy but also on other quality attributes such as robustness and fairness. Unfortunately, application developers rarely assess these attributes despite their importance in BioMed LLMs-based applications due to difficulties in defining the expected output. This study uses Metamorphic Testing (MT) to evaluate the robustness and fairness of the BioMed LLM-based automated ICD coding application. We defined several Metamorphic Relations (MRs) to evaluate these quality attributes systematically. Our results using the MIMIC-III dataset reveal several instances where the application performance is significantly impacted due to various simple manipulations that mimic common mistakes in the input clinical notes. Our findings highlight the necessity of rigorous testing for these metrics to ensure the reliable use of BioMed LLMs in healthcare and medical applications. Further, our research provides a comprehensive framework for such evaluations by leveraging MT, which is helpful to the application developers and contributes to developing more reliable and robust biomedical AI systems.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100564"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic fog computing for enhanced LLM execution in medical applications 在医疗应用中增强LLM执行的动态雾计算

Smart Health Pub Date : 2025-04-02 DOI: 10.1016/j.smhl.2025.100577

Philipp Zagar , Vishnu Ravi , Lauren Aalami , Stephan Krusche , Oliver Aalami , Paul Schmiedmayer

{"title":"Dynamic fog computing for enhanced LLM execution in medical applications","authors":"Philipp Zagar , Vishnu Ravi , Lauren Aalami , Stephan Krusche , Oliver Aalami , Paul Schmiedmayer","doi":"10.1016/j.smhl.2025.100577","DOIUrl":"10.1016/j.smhl.2025.100577","url":null,"abstract":"<div><div>The ability of large language models (LLMs) to process, interpret, and comprehend vast amounts of heterogeneous data presents a significant opportunity to enhance data-driven care delivery. However, the sensitive nature of protected health information (PHI) raises concerns about data privacy and trust in remote LLM platforms. Additionally, the cost of cloud-based artificial intelligence (AI) services remains a barrier to widespread adoption. To address these challenges, we propose shifting the LLM execution environment from centralized, opaque cloud providers to a decentralized and dynamic fog computing architecture. By running open-weight LLMs in more trusted environments, such as a user’s edge device or a fog layer within a local network, we aim to mitigate the privacy, trust, and financial concerns associated with cloud-based LLMs. We introduce <em>SpeziLLM</em>, an open-source framework designed to streamline LLM execution across multiple layers, facilitating seamless integration into digital health applications. To demonstrate its versatility, we showcase <em>SpeziLLM</em> across six digital health applications, highlighting its broad applicability in various healthcare settings.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100577"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heterogeneous sensing based indoor localization leveraging Bayesian prior estimation 基于贝叶斯先验估计的异质感知室内定位

Smart Health Pub Date : 2025-03-28 DOI: 10.1016/j.smhl.2025.100559

Changming Li , Cong Shi , Yingying Chen , David Maluf , Jerome Henry

{"title":"Heterogeneous sensing based indoor localization leveraging Bayesian prior estimation","authors":"Changming Li , Cong Shi , Yingying Chen , David Maluf , Jerome Henry","doi":"10.1016/j.smhl.2025.100559","DOIUrl":"10.1016/j.smhl.2025.100559","url":null,"abstract":"<div><div>Indoor wireless localization is critical for enabling a wide range of mobile and IoT applications, such as elder monitoring, robot navigation, and augmented reality/virtual reality. Current wireless localization techniques rely on homogeneous sensing, utilizing single-modality signals like Bluetooth, WiFi, or mmWave, which are susceptible to in-channel interference, multipath distortions, and environmental variability (e.g., device position and furniture placement changes). In this paper, we design a heterogeneous sensing system that combines wireless signals of multiple modalities to enhance indoor localization accuracy. Through building a Bayesian-based framework, we statistically integrate location fingerprints from various sensing modalities to address the nonlinearities introduced by spatial and temporal fluctuations. Our approach is generalizable and can be applied to existing fingerprinting localization methods based on machine learning algorithms, such as K-nearest neighbors (KNN), support vector machines (SVM), and deep learning models, significantly enhancing the localization performance and robustness. Extensive real-world experiments demonstrate that our system reduces the average localization errors from 2.1 m to 1.23 m, even in the presence of complex environmental dynamics.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100559"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Objective anxiety level classification using unsupervised learning and multimodal physiological signals 目的利用无监督学习和多模态生理信号对焦虑水平进行分类

Smart Health Pub Date : 2025-03-27 DOI: 10.1016/j.smhl.2025.100572

Maxine He , Jonathan Cerna , Roshni Mathew , Jiaqi Zhao , Jennifer Zhao , Ethan Espina , Jean L. Clore , Richard B. Sowers , Elizabeth T. Hsiao-Wecksler , Manuel E. Hernandez

{"title":"Objective anxiety level classification using unsupervised learning and multimodal physiological signals","authors":"Maxine He , Jonathan Cerna , Roshni Mathew , Jiaqi Zhao , Jennifer Zhao , Ethan Espina , Jean L. Clore , Richard B. Sowers , Elizabeth T. Hsiao-Wecksler , Manuel E. Hernandez","doi":"10.1016/j.smhl.2025.100572","DOIUrl":"10.1016/j.smhl.2025.100572","url":null,"abstract":"<div><div>Anxiety disorders are prevalent worldwide and can negatively impact physical and mental health. Thus, the timely detection of changes in anxiety levels is crucial for mental health management. This study used multimodal physiological features from wearable devices to classify anxiety levels across various conditions, normalized by individual baseline responses for personalized analysis. Gaussian Mixture Models clustered data into binary or ternary anxiety levels, interpreted by statistics of self-reported scores and physiological features. Clus ters showed modest alignment with State and Trait Inventory scores and physiological markers and demonstrated task-specific variability. Silhouette scores indicated moderate separation (0.40 for two clusters, 0.14 for three clusters). Binary and three-class classifications using unsupervised learning and leave-one-participant-out validation demonstrated effectiveness, with Support Vector Machine achieving highest accuracies (90.9% and 73.3%). This approach enables objective, personalized anxiety monitoring without relying on subjective labeling.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100572"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intoxication detection from speech using representations learned from self-supervised pre-training 使用自监督预训练学习表征的语音中毒检测

Smart Health Pub Date : 2025-03-27 DOI: 10.1016/j.smhl.2025.100562

Abigail Albuquerque, Samuel Chibuoyim Uche, Emmanuel Agu

{"title":"Intoxication detection from speech using representations learned from self-supervised pre-training","authors":"Abigail Albuquerque, Samuel Chibuoyim Uche, Emmanuel Agu","doi":"10.1016/j.smhl.2025.100562","DOIUrl":"10.1016/j.smhl.2025.100562","url":null,"abstract":"<div><div>Alcohol intoxication is one of the leading causes of death around the globe. Existing approaches to prevent Driving Under the Influence (DUI) are expensive, intrusive, or require external apparatus such as breathalyzers, which the drinker may not possess. Speech is a viable modality for detecting intoxication from changes in vocal patterns. Intoxicated speech is slower, has lower amplitude, and is more prone to errors at the sentence, word, and phonological levels than sober speech. However, intoxication detection from speech is challenging due to high inter- and intra-user variability and the confounding effects of other factors such as fatigue, which may also impair speech. This paper investigates Wav2Vec 2.0, a self-supervised neural network architecture, for intoxication classification from audio. Wav2Vec 2.0 is a Transformer-based model that has demonstrated remarkable performance in various speech-related tasks. It analyzes raw audio directly by applying a multi-head attention mechanism to latent audio representations and was pre-trained on the Librispeech, Libri-Light and EmoDB datasets. The proposed model achieved an unweighted average recall of 73.3%, outperforming state-of-the-art models, highlighting its potential for accurate DUI detection to prevent alcohol-related incidents.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100562"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ADHDSymTracker: Predicting ADHD Symptoms using Apple HealthKit Data ADHDSymTracker：使用Apple HealthKit数据预测ADHD症状

Smart Health Pub Date : 2025-03-27 DOI: 10.1016/j.smhl.2025.100563

Shweta Ware , Allison Baun , Peiyi Wang , Caleb Kwakye , Sofia Dimotsi , Ethan Swift , Nikoloz Gvelesiani , Laura E. Knouse

{"title":"ADHDSymTracker: Predicting ADHD Symptoms using Apple HealthKit Data","authors":"Shweta Ware , Allison Baun , Peiyi Wang , Caleb Kwakye , Sofia Dimotsi , Ethan Swift , Nikoloz Gvelesiani , Laura E. Knouse","doi":"10.1016/j.smhl.2025.100563","DOIUrl":"10.1016/j.smhl.2025.100563","url":null,"abstract":"<div><div>Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent condition that impacts cognitive and behavioral functioning, posing significant challenges for individuals’ academic and daily lives, particularly among college students. The core symptoms are inattention, hyperactivity and impulsivity. Current diagnostic and symptom tracking methods, whether clinician-administered or self-reported, have several limitations, such as recall bias, high costs, and the necessity for manual intervention. This underscores the necessity for an objective, accurate, and cost-effective tool for ADHD diagnosis that requires minimal manual intervention. To address this issue, we propose a novel approach, ADHDSymTracker, which uses Apple HealthKit data to predict ADHD symptoms. We calculated behavioral features using data collected from 38 college-age students including some with ADHD and developed a suite of machine learning models for ADHD symptom prediction. Our results from ADHDSymTracker indicate that most symptoms can be predicted with reasonable accuracy, achieving an <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> score as high as 0.72, rendering it a promising solution for automatic and continuous ADHD monitoring.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100563"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A low-channel EEG-to-speech conversion approach for assisting people with communication disorders 一种帮助有沟通障碍人士的低通道脑电图-语言转换方法

Smart Health Pub Date : 2025-03-26 DOI: 10.1016/j.smhl.2025.100568

Kunning Shen , Huining Li

引用次数: 0

Background parenchymal uptake classification using deep transfer learning on digital mammograms 背景：基于深度迁移学习的数字化乳房x光片实质摄取分类

Smart Health Pub Date : 2025-03-26 DOI: 10.1016/j.smhl.2025.100573

Xudong Liu , Christopher Scott , Imon Banerjee , Celine Vachon , Carrie Hruska

{"title":"Background parenchymal uptake classification using deep transfer learning on digital mammograms","authors":"Xudong Liu , Christopher Scott , Imon Banerjee , Celine Vachon , Carrie Hruska","doi":"10.1016/j.smhl.2025.100573","DOIUrl":"10.1016/j.smhl.2025.100573","url":null,"abstract":"<div><div>Background parenchymal uptake (BPU) in fibroglandular tissue on a molecular breast image (MBI) has been shown to be a strong risk factor for breast cancer and complementary to mammographic density. However, MBI is generally performed on women with dense breasts and only available at institutions with nuclear medicine capabilities, limiting the utility of this measure in routine breast screening and risk assessment. Digital mammography is used for routine breast screening. Our goal was to evaluate whether BPU features could be identified from digital mammograms (DMs) using deep transfer learning. Specifically, we identified a cohort of about 2000 women from a breast screening center who had DM and MBI performed at the same time period and trained models on DMs to classify BPU categories. We consider two types of classification problems in this work: a five-category classification of BPU and two combined classes. We designed and implemented machine learning algorithms leveraging state-of-the-art pre-trained deep neural networks, evaluated these algorithms on the collected data based using metrics such as accuracy, F1-score, and AUROC, and provided visual explanations using saliency mapping and gradient-weighted class activation mapping (GradCAM). Our results show that, among the experimented models, WideResNet-50 demonstrates the best performance on a hold-out test set with 58% accuracy, 0.82 micro-average AUROC and 0.72 macro-average AUROC on the five-category classification, while ResNet-18 comes out on top with 77% accuracy, 0.86 AUROC and 0.77 F1-score on the binary categorization. We also found that incorporating age, body mass index (BMI) and menopausal status improved classification of BPU compared to DM alone.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100573"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0