Smart Health最新文献

{"title":"Breath as a biomarker: A survey of contact and contactless applications and approaches in respiratory monitoring","authors":"Almustapha A. Wakili,&nbsp;Babajide J. Asaju,&nbsp;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":"2025-06-01","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}

{"title":"Generalized multisensor wearable signal fusion for emotion recognition from noisy and incomplete data","authors":"Vamsi Kumar Naidu Pallapothula ,&nbsp;Sidharth Anand ,&nbsp;Sreyasee Das Bhattacharjee,&nbsp;Junsong Yuan","doi":"10.1016/j.smhl.2025.100571","DOIUrl":"10.1016/j.smhl.2025.100571","url":null,"abstract":"<div><div>Continual real-time monitoring of users’ health via noninvasive wearable devices (e.g., smartwatch, smartphone) demonstrates significant potential to enhance human well-being in everyday life. However, due to respective sampling rates, noise sensitivity, and data types, the inherent heterogeneity of the signals received from multiple sensors make the task of biosignal-based emotion recognition both complex and time-consuming. While how to optimally fuse multimode information (where each sensor produces a unique mode-specific input signal) to ensure a reliable inference performance remains difficult, the particular challenges in this problem setting is primarily threefold: (1) The data availability is limited due to several unique person/device-specific properties and high cost of labeling; (2) The acquired signals from wearable devices are often noisy or may as well be lossy due to users’ personal lifestyle choices or environmental interferences; (3) Due to several intra-individual and inter-individual signal variabilities, enabling model generalizability is always difficult. To this end, we propose a general-purpose multisensor fusion network, <em>GM-FuseNet</em> that can seamlessly integrate and transform multi-sensor signal information for a variety of tasks. Unlike a majority of existing works, which rely on a fundamental assumption that full multi-mode query information is present during inference, <em>GM-FuseNet</em>’s first-level preface multimodal transformer module is explicitly designed to enhance both unimodal and multimodal performance in the presence of partial modality details. We also utilize an effective <em>multimodal temporal correlation loss</em> that aligns the unimode signals pairwise in the temporal domain and encourages the model to learn the temporal correlation across multiple sensor-specific signals. Extensive evaluation using two public datasets WESAD and CASE reports outperformance (<span><math><mrow><mn>1</mn><mtext>–</mtext><mn>4</mn><mtext>%</mtext></mrow></math></span>) of the proposed <em>GM-FuseNet</em> against state-of-the-art supervised or self-supervised models while delivering a consistently robust generalization all-across. Additionally, by reporting another <span><math><mrow><mn>2</mn><mtext>–</mtext><mn>4</mn><mtext>%</mtext></mrow></math></span> improved accuracy and F1-scores, <em>GM-FuseNet</em> also demonstrates a significant promise in handling a variety of test environments including the missing and noisy multisensor query signals.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100571"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738104","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}

{"title":"Background parenchymal uptake classification using deep transfer learning on digital mammograms","authors":"Xudong Liu ,&nbsp;Christopher Scott ,&nbsp;Imon Banerjee ,&nbsp;Celine Vachon ,&nbsp;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":"2025-06-01","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}

{"title":"SNOMED CT ontology multi-relation classification by using knowledge embedding in neural network","authors":"Bofan He,&nbsp;Jerry Q. Cheng,&nbsp;Huanying Gu","doi":"10.1016/j.smhl.2025.100560","DOIUrl":"10.1016/j.smhl.2025.100560","url":null,"abstract":"<div><div>SNOMED CT is a widely recognized healthcare terminology designed to comprehensively represent clinical knowledge. Identifying missing or incorrect relationships between medical concepts is crucial for enhancing the scope and quality of this ontology, thereby improving healthcare analytics and decision support. In this study, we propose a novel multi-link prediction approach that utilizes knowledge graph embeddings and neural networks to infer missing relationships within the SNOMED CT knowledge graph. By utilizing TransE, we train embeddings for triples (concept, relation, concept) and develop a multi-head classifier to predict relationship types based solely on concept pairs. With an embedding dimension of 200, a batch size of 128, and 10 epochs, we achieved the highest test accuracy of 91.96% in relationships prediction tasks. This study demonstrates an optimal balance between efficiency, generalization, and representational capacity. By expanding on existing methodologies, this work offers insights into practical applications for ontology enrichment and contributes to the ongoing advancement of predictive models in healthcare informatics. Furthermore, it highlights the potential scalability of the approach, providing a framework that can be extended to other knowledge graphs and domains.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100560"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760190","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}

{"title":"Mobile app-based study of driving behaviors under the influence of cannabis","authors":"Honglu Li ,&nbsp;Bin Han ,&nbsp;Cong Shi ,&nbsp;Yan Wang ,&nbsp;Tammy Chung ,&nbsp;Yingying Chen","doi":"10.1016/j.smhl.2025.100558","DOIUrl":"10.1016/j.smhl.2025.100558","url":null,"abstract":"<div><div>Cannabis use has become increasingly prevalent due to evolving legal and societal attitudes, raising concerns about its influence on public safety, particularly in driving. Existing studies mostly rely on simulators or specialized equipment, which do not capture the complexities of real-world driving and pose cost and scalability issues. In this paper, we investigate the effects of cannabis on driving behavior using participants’ smartphones to gather data in natural settings. Our method focuses on three critical behaviors: weaving &amp; swerving, wide turning, and hard braking. We propose a two-step segmentation algorithm for processing continuous motion sensor data and use threshold-based methods for efficient detection. A custom application autonomously records driving events during actual road scenarios. On-road experiments with 9 participants who consumed cannabis under controlled conditions reveal a correlation between cannabis use and altered driving behaviors, with significant effects emerging approximately 2<span><math><mo>∼</mo></math></span>3 h after consumption.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100558"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738103","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}

{"title":"Advancing real-world applications: A scoping review on emerging wearable technologies for recognizing activities of daily living","authors":"Mustafa Elhadi Ahmed ,&nbsp;Hongnian Yu ,&nbsp;Michael Vassallo ,&nbsp;Pelagia Koufaki","doi":"10.1016/j.smhl.2025.100555","DOIUrl":"10.1016/j.smhl.2025.100555","url":null,"abstract":"<div><div>Wearable technologies for Activities of Daily Living (ADL) recognition have emerged as a crucial area of research, driven by the global rise in aging populations and the increase in chronic diseases. These technologies offer significant benefits for healthcare by enabling continuous monitoring and early detection of health issues. However, the field of ADL recognition with wearables remains under-explored in key areas such as user variability and data acquisition methodologies. This review aims to provide a comprehensive overview of recent advancements in ADL recognition using wearable devices, with a particular focus on commercially available devices. We systematically analyzed 157 studies from six databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, narrowing our focus to 77 articles that utilized proprietary datasets. These studies revealed three main categories of wearables: prototype devices (40 %), commercial research-grade devices (32 %), and consumer-grade devices (28 %) adapted for ADL recognition. Additionally, various detection algorithms were identified, with 31 % of studies utilizing basic machine learning techniques, 40 % employing advanced deep learning methods, and the remainder exploring ensemble learning and transfer learning approaches. Our findings underscore the growing adoption of accessible, commercial devices for both research and clinical applications. Furthermore, we identified two key areas for future research: the development of user-centered data preparation techniques to account for variability in ADL performance, and the enhancement of wearable technologies to better align with the practical needs of healthcare systems. These advancements are expected to enhance the usability and efficiency of wearables in improving patient care and healthcare management.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100555"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696895","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}

{"title":"HealthQ: Unveiling questioning capabilities of LLM chains in healthcare conversations","authors":"Ziyu Wang ,&nbsp;Hao Li ,&nbsp;Di Huang ,&nbsp;Hye-Sung Kim ,&nbsp;Chae-Won Shin ,&nbsp;Amir M. Rahmani","doi":"10.1016/j.smhl.2025.100570","DOIUrl":"10.1016/j.smhl.2025.100570","url":null,"abstract":"<div><div>Effective patient care in digital healthcare requires large language models (LLMs) that not only answer questions but also actively gather critical information through well-crafted inquiries. This paper introduces HealthQ, a novel framework for evaluating the questioning capabilities of LLM healthcare chains. By implementing advanced LLM chains, including Retrieval-Augmented Generation (RAG), Chain of Thought (CoT), and reflective chains, HealthQ assesses how effectively these chains elicit comprehensive and relevant patient information. To achieve this, we integrate an LLM judge to evaluate generated questions across metrics such as specificity, relevance, and usefulness, while aligning these evaluations with traditional Natural Language Processing (NLP) metrics like ROUGE and Named Entity Recognition (NER)-based set comparisons. We validate HealthQ using two custom datasets constructed from public medical datasets, ChatDoctor and MTS-Dialog, and demonstrate its robustness across multiple LLM judge models, including GPT-3.5, GPT-4, and Claude. Our contributions are threefold: we present the first systematic framework for assessing questioning capabilities in healthcare conversations, establish a model-agnostic evaluation methodology, and provide empirical evidence linking high-quality questions to improved patient information elicitation.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100570"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704085","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}

{"title":"Heterogeneous sensing based indoor localization leveraging Bayesian prior estimation","authors":"Changming Li ,&nbsp;Cong Shi ,&nbsp;Yingying Chen ,&nbsp;David Maluf ,&nbsp;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":"2025-06-01","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}

{"title":"Metamorphic Testing for Robustness and Fairness Evaluation of LLM-based Automated ICD Coding Applications","authors":"Guna Sekaran Jaganathan,&nbsp;Indika Kahanda,&nbsp;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":"2025-06-01","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}

{"title":"Automated supplement correction in passive urine color measurement device for real-time hydration testing","authors":"Kelvin M. Frazier,&nbsp;Brian F. Bender","doi":"10.1016/j.smhl.2025.100554","DOIUrl":"10.1016/j.smhl.2025.100554","url":null,"abstract":"<div><div>Monitoring urine color as a means of assessing hydration status has long been a recommended technique for active populations like athletes, military personnel, and outdoor laborers. Urine color correlates well to urine concentration and is a simple, non-invasive practice. However, currently this approach is subjective, and errors arise in variation in ambient lighting conditions, comparator colors used, and individual perception. In addition, certain supplements such as riboflavin (vitamin B2) and beet juice are known to distort urine color and can confound hydration assessment. An automated urinalysis device (InFlow) was developed to measure urine color, an index of hydration status, in real-time during urination in the presence of these supplements. Machine learning techniques were used to reduce mean absolute hydration assessment error from riboflavin-derived color skew from 2.50 ± 0.37 to 0.85 (±0.06) color units on a 7-point color chart scale compared to a commercial colorimeter. In the absence of supplements and in the samples spiked with beet juice the InFlow device produced a mean absolute error of 0.48 (±0.06) color units. Finally, we demonstrate the feasibility of detecting myoglobinuria for potential future use in rhabdomyolysis screening. Our results show the InFlow device provides a novel approach with appropriate accuracy for standardizing hydration assessment via urinalysis in environments with high testing frequency demands in the presence of common urine color interferents including riboflavin and beet juice.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100554"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579868","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}