Smart Health最新文献_第5页

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 : 2025-06-01 Epub 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":"2025-06-01","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

An adaptive multimodal fusion framework for smartphone-based medication adherence monitoring of Parkinson’s disease 基于智能手机的帕金森病药物依从性监测的自适应多模式融合框架

Smart Health Pub Date : 2025-06-01 Epub Date: 2025-03-25 DOI: 10.1016/j.smhl.2025.100561

Chongxin Zhong , Jinyuan Jia , Huining Li

{"title":"An adaptive multimodal fusion framework for smartphone-based medication adherence monitoring of Parkinson’s disease","authors":"Chongxin Zhong , Jinyuan Jia , Huining Li","doi":"10.1016/j.smhl.2025.100561","DOIUrl":"10.1016/j.smhl.2025.100561","url":null,"abstract":"<div><div>Ensuring medication adherence for Parkinson’s disease (PD) patients is crucial to relieve patients’ symptoms and better customizing regimens according to patient’s clinical responses. However, traditional self-management approaches are often error-prone and have limited effectiveness in improving adherence. While smartphone-based solutions have been introduced to monitor various PD metrics, including medication adherence, these methods often rely on single-modality data or fail to fully leverage the advantages of multimodal integration. To address the issues, we present an adaptive multimodal fusion framework for monitoring medication adherence of PD based on a smartphone. Specifically, we segment and transform raw data from sensors to spectrograms. Then, we integrate multimodal data with quantification of their qualities and perform gradient modulation based on the contribution of each modality. Afterward, we monitor medication adherence in PD patients by detecting their medicine intake status. We evaluate the performance with the dataset from daily-life scenarios involving 455 patients. The results show that our work can achieve around 94% accuracy in medication adherence monitoring, indicating that our proposed framework is a promising tool to facilitate medication adherence monitoring in PD patients’ daily lives.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100561"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704184","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

Privacy-preserving LLM-based chatbots for hypertensive patient self-management 保护隐私的基于llm的高血压患者自我管理聊天机器人

Smart Health Pub Date : 2025-06-01 Epub Date: 2025-03-07 DOI: 10.1016/j.smhl.2025.100552

Sara Montagna , Stefano Ferretti , Lorenz Cuno Klopfenstein , Michelangelo Ungolo , Martino Francesco Pengo , Gianluca Aguzzi , Matteo Magnini

{"title":"Privacy-preserving LLM-based chatbots for hypertensive patient self-management","authors":"Sara Montagna , Stefano Ferretti , Lorenz Cuno Klopfenstein , Michelangelo Ungolo , Martino Francesco Pengo , Gianluca Aguzzi , Matteo Magnini","doi":"10.1016/j.smhl.2025.100552","DOIUrl":"10.1016/j.smhl.2025.100552","url":null,"abstract":"<div><div>Medical chatbots are becoming a basic component in telemedicine, propelled by advancements in Large Language Models (LLMs). However, LLMs’ integration into clinical settings comes with several issues, with privacy concerns being particularly significant.</div><div>The paper proposes a tailored architectural solution and an information workflow that address privacy issues, while preserving the benefits of LLMs. We examine two solutions to prevent the disclosure of sensitive information: <em>(i)</em> a filtering mechanism that processes sensitive data locally but leverage a robust OpenAI’s online LLM for engaging with the user effectively, and <em>(ii)</em> a fully local deployment of open-source LLMs. The effectiveness of these solutions is assessed in the context of hypertension management across various tasks, ranging from intent recognition to reliable and emphatic conversation. Interestingly, while the first solution proved to be more robust in intent recognition, an evaluation by domain experts of the models’ responses, based on reliability and empathetic principles, revealed that two out of six open LLMs received the highest scores.</div><div>The study underscores the viability of incorporating LLMs into medical chatbots. In particular, our findings suggest that open LLMs can offer a privacy-preserving, yet promising, alternative to external LLM services, ensuring safer and more reliable telemedicine practices. Future efforts will focus on fine-tuning local models to enhance their performance across all tasks.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100552"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601759","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

Quantum contrastive learning for human activity recognition 人类活动识别的量子对比学习

Smart Health Pub Date : 2025-06-01 Epub Date: 2025-03-24 DOI: 10.1016/j.smhl.2025.100574

Yanhui Ren , Di Wang , Lingling An , Shiwen Mao , Xuyu Wang

{"title":"Quantum contrastive learning for human activity recognition","authors":"Yanhui Ren , Di Wang , Lingling An , Shiwen Mao , Xuyu Wang","doi":"10.1016/j.smhl.2025.100574","DOIUrl":"10.1016/j.smhl.2025.100574","url":null,"abstract":"<div><div>Deep learning techniques have been widely used for human activity recognition (HAR) applications. The major challenge lies in obtaining high-quality, large-scale labeled sensor datasets. However, unlike datasets such as images or text, HAR sensor datasets are non-intuitive and uninterpretable, making manual labeling extremely difficult. Self-supervised learning has emerged to address this problem, which can learn from large-scale unlabeled datasets that are easier to collect. Nevertheless, self-supervised learning has the increased computational cost and the demand for larger deep neural networks. Recently, quantum machine learning has attracted widespread attention due to its powerful computational capability and feature extraction ability. In this paper, we aim to address this classical hardware bottleneck using quantum machine learning techniques. We propose QCLHAR, a quantum contrastive learning framework for HAR, which combines quantum machine learning techniques with contrastive learning to learn better latent representations. We evaluate the feasibility of the proposed framework on six publicly available datasets for HAR. The experimental results demonstrate the effectiveness of the framework for HAR, which can surpass or match the precision of classical contrastive learning with fewer parameters. This validates the effectiveness of our approach and demonstrates the significant potential of quantum technology in addressing the challenges associated with the scarcity of labeled sensory data.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100574"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714431","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

Improving gastric lesion detection with synthetic images from diffusion models 利用扩散模型合成图像改进胃病变检测

Smart Health Pub Date : 2025-06-01 Epub Date: 2025-03-25 DOI: 10.1016/j.smhl.2025.100569

Yanhua Si , Yingyun Yang , Qilei Chen , Zinan Xiong , Yu Cao , Xinwen Fu , Benyuan Liu , Aiming Yang

{"title":"Improving gastric lesion detection with synthetic images from diffusion models","authors":"Yanhua Si , Yingyun Yang , Qilei Chen , Zinan Xiong , Yu Cao , Xinwen Fu , Benyuan Liu , Aiming Yang","doi":"10.1016/j.smhl.2025.100569","DOIUrl":"10.1016/j.smhl.2025.100569","url":null,"abstract":"<div><div>In the application of deep learning for gastric cancer detection, the quality of the data set is as important as, if not more, the design of the network architecture. However, obtaining labeled data, especially in fields such as medical imaging to detect gastric cancer, can be expensive and challenging. This scarcity is exacerbated by stringent privacy regulations and the need for annotations by specialists. Conventional methods of data augmentation fall short due to the complexities of medical imagery. In this paper, we explore the use of diffusion models to generate synthetic medical images for the detection of gastric cancer. We evaluate their capability to produce realistic images that can augment small datasets, potentially enhancing the accuracy and robustness of detection algorithms. By training diffusion models on existing gastric cancer data and producing new images, our aim is to expand these datasets, thereby enhancing the efficiency of deep learning model training to achieve better precision and generalization in lesion detection. Our findings indicate that images generated by diffusion models significantly mitigate the issue of data scarcity, advancing the field of deep learning in medical imaging.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100569"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738102","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

Human AI Collaboration Framework for Detecting Mental Illness Causes from Social Media 从社交媒体中检测精神疾病原因的人类人工智能协作框架

Smart Health Pub Date : 2025-06-01 Epub Date: 2025-03-23 DOI: 10.1016/j.smhl.2025.100566

Abm Adnan Azmee, Francis Nweke, Mason Pederson, Md Abdullah Al Hafiz Khan, Yong Pei

{"title":"Human AI Collaboration Framework for Detecting Mental Illness Causes from Social Media","authors":"Abm Adnan Azmee, Francis Nweke, Mason Pederson, Md Abdullah Al Hafiz Khan, Yong Pei","doi":"10.1016/j.smhl.2025.100566","DOIUrl":"10.1016/j.smhl.2025.100566","url":null,"abstract":"<div><div>Mental health is a critical aspect of our overall well-being. Mental illness refers to conditions that impact an individual’s psychological state, resulting in considerable distress and limitations in functioning day-to-day tasks. Due to the progress of technology, social media has emerged as a platform for individuals to share their thoughts and emotions. The psychological state of individuals can be accessed with the help of data from these platforms. However, it is challenging for conventional machine learning models to analyze the diverse linguistic contexts of social media data. Moreover, to effectively analyze the data, we need the support of human experts. In this work, we propose a novel human AI-collaboration framework that leverages the strength of human expertise and artificial intelligence (AI) to overcome these challenges. Our proposed framework utilizes multi-level data along with feedback from human experts to identify the causes behind mental illness. The efficacy and effectiveness of our proposed model are shown by extensive evaluation on Reddit data. Experimental results demonstrate that our proposed model outperforms the baseline model by 3 to 17% performance improvement.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100566"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725639","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

Continuous prediction of user dropout in a mobile mental health intervention program: An exploratory machine learning approach 移动心理健康干预项目中用户退出的连续预测：一种探索性机器学习方法

Smart Health Pub Date : 2025-06-01 Epub Date: 2025-03-25 DOI: 10.1016/j.smhl.2025.100565

Pinxiang Wang , Hanqi Chen , Zhouyu Li , Wenyao Xu , Yu-Ping Chang , Huining Li

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 : 2025-06-01 Epub 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":"2025-06-01","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

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 : 2025-06-01 Epub 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":"2025-06-01","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 : 2025-06-01 Epub 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":"2025-06-01","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