Smart Health最新文献

Enhancing latent space representation in Adversarial Autoencoders for exercise recognition: A data augmentation perspective using low-cost sensors 增强运动识别对抗性自编码器的潜在空间表示：使用低成本传感器的数据增强视角

Q2 Health Professions

Smart Health

Pub Date : 2026-01-27 DOI: 10.1016/j.smhl.2026.100635

Vincent Hernandez, Gentiane Venture

Exercise monitoring in the context of Human Activity Recognition (HAR) is essential for delivering immediate feedback and facilitating the analysis of movement. When combined with data dimensionality reduction, it can offer deeper insights into movement patterns, thereby aiding in the development of training programs. This study investigates the impact of data augmentation and the number of participants in the training data on the accuracy of 2D latent space representations generated by an Adversarial AutoEncoder (AAE).

In this study, data from the Wii Balance Board (WiiBB) and Inertial Measurement Units (IMUs) placed on each forearm and hip were collected from 20 participants. Experiments were performed for upper and lower body exercises, with the accuracy of the latent space representation analyzed by varying the number of participants in the training set from 2 to 12 with and without data augmentation.

The results demonstrate that the incorporation of data augmentation significantly improves the accuracy of the latent space representation of AAE. For example, using only two participants in the training set, data augmentation improves test accuracy by 10.2% and 4.4% for WiiBB data and IMU, respectively, for lower body exercises, while upper body exercises showed improvements of 6.8% and 3.1% respectively.

These findings show how data augmentation can mitigate the limitations of small training datasets significantly improving latent space representations for HAR applications. This study emphasizes the importance of combining data augmentation strategies and sensor types to achieve reliable and interpretable results in remote rehabilitation systems.

在人类活动识别（HAR）的背景下，运动监测对于提供即时反馈和促进运动分析至关重要。当与数据降维相结合时，它可以更深入地了解运动模式，从而帮助制定训练计划。本研究探讨了数据增强和训练数据中参与者数量对对抗自动编码器（AAE）生成的二维潜在空间表示精度的影响。在这项研究中，从20名参与者的前臂和髋部的Wii平衡板（WiiBB）和惯性测量单元（imu）收集数据。对上半身和下半身练习进行了实验，并通过将训练集中的参与者人数从2变到12，在数据增强和不增强的情况下，分析了潜在空间表示的准确性。结果表明，数据增强的引入显著提高了AAE潜在空间表示的准确性。例如，在训练集中只使用两名参与者时，对于下半身练习，数据增强使WiiBB数据和IMU的测试准确率分别提高了10.2%和4.4%，而上半身练习则分别提高了6.8%和3.1%。这些发现表明，数据增强可以减轻小型训练数据集的局限性，显著改善HAR应用的潜在空间表示。本研究强调了将数据增强策略和传感器类型相结合的重要性，以在远程康复系统中获得可靠和可解释的结果。

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引用次数: 0

Patient-specific deep offline artificial pancreas for blood glucose regulation in type 1 diabetes 1型糖尿病患者特异性深度脱机人工胰腺血糖调节

Q2 Health Professions

Smart Health

Pub Date : 2026-01-17 DOI: 10.1016/j.smhl.2026.100633

Yixiang Deng , Kevin Arao , Christos S. Mantzoros , George Em Karniadakis

Due to insufficient insulin secretion, patients with type 1 diabetes mellitus (T1DM) are prone to blood glucose fluctuations ranging from hypoglycemia to hyperglycemia. While dangerous hypoglycemia may lead to coma immediately, chronic hyperglycemia increases patients’ risks for cardiorenal and vascular diseases in the long run. In principle, an artificial pancreas – a closed-loop insulin delivery system requiring patients to manually input insulin dosage according to the upcoming meals – could supply exogenous insulin to control the glucose levels and hence reduce the risks from hyperglycemia. However, insulin overdosing in some type 1 diabetic patients, who are physically active, can lead to unexpected hypoglycemia beyond the control of the common artificial pancreas. Therefore, it is important to take into account the glucose decrease due to physical exercise when designing the next-generation artificial pancreas. In this work, we develop a framework integrating systems biology-informed neural networks (SBINN), deep reinforcement learning (RL) algorithms, and T1DM data collected from wearable devices, to automate insulin dosing for patients. In particular, we build patient-specific computational models using SBINN to mimic the glucose-insulin dynamics for a few patients from the dataset, by simultaneously considering patient-specific carbohydrate intake and physical exercise intensity. Our patient-specific artificial pancreas, based on two deep RL algorithms, provided better insulin dosage, leading to safer glucose levels compared to those in the original dataset.

1型糖尿病（T1DM）患者由于胰岛素分泌不足，容易出现低血糖到高血糖的血糖波动。危险的低血糖可能会立即导致昏迷，而长期的高血糖会增加患者患心肾和血管疾病的风险。原则上，人工胰腺——一个闭环胰岛素输送系统，需要患者根据即将到来的饮食手动输入胰岛素剂量——可以提供外源性胰岛素来控制葡萄糖水平，从而降低高血糖的风险。然而，胰岛素过量的一些1型糖尿病患者，谁是体力活动，可导致意外的低血糖超出控制的普通人工胰腺。因此，在设计下一代人工胰腺时，考虑到运动导致的血糖下降是很重要的。在这项工作中，我们开发了一个框架，集成了系统生物学信息神经网络（SBINN）、深度强化学习（RL）算法和从可穿戴设备收集的T1DM数据，以自动为患者给胰岛素。特别是，我们使用SBINN建立了患者特异性计算模型，通过同时考虑患者特异性碳水化合物摄入量和体育锻炼强度，模拟数据集中少数患者的葡萄糖-胰岛素动力学。与原始数据集相比，我们基于两种深度强化学习算法的患者特异性人工胰腺提供了更好的胰岛素剂量，导致更安全的血糖水平。

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引用次数: 0

Mental health risk prediction in autoimmune patients in primary care: A class-sensitive machine learning approach 初级保健中自身免疫患者的心理健康风险预测：一种类别敏感的机器学习方法

Q2 Health Professions

Smart Health

Pub Date : 2026-01-16 DOI: 10.1016/j.smhl.2026.100634

Mariachiara Di Cosmo , Sara Campanella , Michele Bernardini , Adriano Mancini , Lorenzo Palma

Patients with autoimmune diseases are at increased risk of developing psychological conditions such as anxiety and depression. Timely detection of these mental health disorders is essential for effective intervention, yet remains difficult due to the overlap and subtle presentation of symptoms. In this study, we propose a machine learning (ML) framework for early prediction of anxiety and depression in autoimmune patients using routine clinical and demographic features in primary care. We develop an XGBoost-based model enhanced with custom loss functions specifically designed to handle class imbalance and semantic similarity between mental health outcomes. Experimental results demonstrate that the proposed penalty terms improve standard XGBoost performance in identifying minority classes and minimizing confusion between similar categories, by reaching a maximum statistically significant macro-Recall gain of 4.03% (p

<

0.05) for the Logit Distance Exponential Penalty term. This work contributes to developing data-driven tools for mental health risk assessment, with potential applications in personalized care and digital screening for vulnerable clinical populations.

患有自身免疫性疾病的患者患焦虑和抑郁等心理疾病的风险增加。及时发现这些精神健康障碍对于有效干预至关重要，但由于症状的重叠和微妙表现，仍然很困难。在这项研究中，我们提出了一个机器学习（ML）框架，用于早期预测自身免疫患者的焦虑和抑郁，使用常规临床和初级保健的人口统计学特征。我们开发了一个基于xgboost的模型，增强了自定义损失函数，专门设计用于处理类别不平衡和心理健康结果之间的语义相似性。实验结果表明，通过对Logit距离指数惩罚项达到4.03% （p<0.05）的最大统计显着宏观召回增益，所提出的惩罚项提高了标准XGBoost在识别少数类别和最小化相似类别之间混淆方面的性能。这项工作有助于开发数据驱动的心理健康风险评估工具，并有可能应用于针对弱势临床人群的个性化护理和数字筛查。

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引用次数: 0

AI-powered play assessment approach using video language models: A feasibility study 使用视频语言模型的ai游戏评估方法：可行性研究

Q2 Health Professions

Smart Health

Pub Date : 2026-01-03 DOI: 10.1016/j.smhl.2025.100632

Amiya Waldman-Levi , Dengyi Liu , Chana Cunin , Vanessa Murad , Honggang Wang

Social-behavioral observation-based assessments are integral to clinical practice and research in health and psychology. However, these methods are often time-intensive and prone to human error, bias, and inconsistency. Deep neural networks (DNNs), a class of machine learning models, offer distinct advantages in healthcare assessments due to their advanced ability to process complex data with greater accuracy than traditional approaches, such as tree-based models. We developed innovative AI-powered software that integrates DNNs with computer vision techniques to analyze parent–child joint play interactions. Our objective was to utilize Video Large Language Models (Video LLMs) to automatically score a validated parent–child play scale (PC-SCP). Using convenience sampling, we recruited 37 mother–child dyads, including both neurotypical and neurodiverse children aged 1–6 years. Following eligibility screening and consent procedures, data collection involved recording 10–15-minute videos of parent–child interactions at home, which were then manually scored using the Parent/Caregiver Support of Children’s Playfulness criteria. We trained and evaluated several DNN models, including Qwen2.5VL, to identify and track parental behaviors in video frames and automatically score the interactions based on the PC-SCP guidelines. Among the models evaluated, our fine-tuned Qwen2.5VL achieved an accuracy of 38.2% and a best-5 accuracy of 61.3%, demonstrating promising potential for automating the scoring of social-behavioral assessments. This novel application of AI represents a significant advancement toward more efficient, objective, and consistent behavioral assessments in both clinical and research settings.

基于社会行为观察的评估是健康和心理学临床实践和研究的组成部分。然而，这些方法通常是耗时的，并且容易出现人为错误、偏见和不一致。深度神经网络（dnn）是一类机器学习模型，在医疗保健评估中具有独特的优势，因为它们具有比传统方法（如基于树的模型）更准确地处理复杂数据的先进能力。我们开发了创新的人工智能软件，将深度神经网络与计算机视觉技术相结合，分析亲子共同玩耍的互动。我们的目标是利用视频大型语言模型（Video LLMs）自动对经过验证的亲子游戏量表（PC-SCP）进行评分。采用方便抽样，我们招募了37对母子，包括1-6岁的神经正常和神经多样化儿童。在资格筛选和同意程序之后，数据收集包括在家中录制10 - 15分钟的亲子互动视频，然后使用家长/照顾者支持儿童玩耍标准手动评分。我们训练并评估了几个DNN模型，包括Qwen2.5VL，以识别和跟踪视频帧中的父母行为，并根据PC-SCP指南自动对互动进行评分。在评估的模型中，我们优化的Qwen2.5VL模型的准确率为38.2%，最佳5分准确率为61.3%，显示了社会行为评估自动化评分的潜力。这种人工智能的新应用代表了在临床和研究环境中更有效、客观和一致的行为评估方面的重大进步。

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引用次数: 0

Integrating AI into healthcare systems: A multivocal literature review 将人工智能整合到医疗保健系统：多语种文献综述

Q2 Health Professions

Smart Health

Pub Date : 2025-12-16 DOI: 10.1016/j.smhl.2025.100631

Giulio Mallardi, Fabio Calefato, Luigi Quaranta, Filippo Lanubile

The integration of artificial intelligence (AI) into healthcare systems promises to improve patient care, enhance operational efficiency, and facilitate personalized medicine. The goal of this paper is to provide a comprehensive review of the current challenges that hinder the seamless adoption of AI in healthcare. Additionally, the paper aims to delineate the best practices for achieving optimal integration of AI within the medical domain. To achieve these objectives, we employ a Multivocal Literature Review (MLR), a systematic literature review methodology that incorporates both peer-reviewed publications and non-peer-reviewed sources, including technical blog posts and white papers. Substantial evidence in the literature points to challenges related to data quality, model bias, interoperability, patient privacy, and the susceptibility of AI systems to adversarial attacks. Additionally, there is growing awareness of challenges such as the distributional shift between training and production data, as well as the critical need for continuous monitoring and retraining of AI models within dynamic clinical settings. Based on our review, we advocate for the adoption of best practices aimed at mitigating the identified challenges, including rigorous model evaluation, standardization of data practices, and promotion of interdisciplinary collaboration. Furthermore, we emphasize the need for responsible AI that aligns with principles of fairness, transparency, security, and reliability, underscoring the importance of multi-stakeholder engagement.

将人工智能（AI）集成到医疗保健系统中，有望改善患者护理，提高运营效率，并促进个性化医疗。本文的目标是全面回顾当前阻碍人工智能在医疗保健领域无缝采用的挑战。此外，本文旨在描述在医疗领域实现人工智能最佳集成的最佳实践。为了实现这些目标，我们采用了多声音文献综述（MLR），这是一种系统的文献综述方法，结合了同行评审的出版物和非同行评审的来源，包括技术博客文章和白皮书。文献中的大量证据指出了与数据质量、模型偏差、互操作性、患者隐私以及人工智能系统对对抗性攻击的易感性相关的挑战。此外，人们越来越意识到挑战，例如培训和生产数据之间的分布变化，以及在动态临床环境中持续监测和再培训人工智能模型的迫切需要。根据我们的审查，我们提倡采用旨在减轻已确定挑战的最佳实践，包括严格的模型评估、数据实践的标准化和促进跨学科合作。此外，我们强调需要负责任的人工智能，符合公平、透明、安全和可靠的原则，强调多方利益相关者参与的重要性。

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引用次数: 0

Erratum to “Real-time face mask position recognition system based on MobileNet model” [Smart Health 28 (2023) 100382] “基于MobileNet模型的实时口罩位置识别系统”勘误表[智能健康28 (2023)100382]

Q2 Health Professions

Smart Health

Pub Date : 2025-12-01 DOI: 10.1016/j.smhl.2025.100619

Md Hafizur Rahman , Mir Kanon Ara Jannat , Md Shafiqul Islam , Giuliano Grossi , Sathya Bursic , Md Aktaruzzaman

引用次数: 0

From pre-treatment to post-operative care: Leveraging digital twins for precision surgery transformation 从术前到术后护理：利用数字双胞胎实现精准手术转型

Q2 Health Professions

Smart Health

Pub Date : 2025-11-22 DOI: 10.1016/j.smhl.2025.100620

Farnaz Dehghan , Seyed Mojtaba Hosseini Bamakan , Mahboubeh Mirzabagheri , Alireza Naser Sadrabadi

Digital twin (DT) technology offers transformative potential for the healthcare sector by enabling virtual models that support data-driven diagnostics, treatment planning, and preventive care. This paper presents a robust framework aimed at unlocking the full potential of digital twins across all facets of healthcare delivery. The process begins with the pre-treatment phase, during which patient data from multiple sources are integrated to construct digital twin models, enabling predictive analytics and aiding in surgical planning. Moving into surgery, DTs prove invaluable by simulating procedures, offering real-time guidance, and facilitating remote collaboration among medical experts. In the postoperative phase, they support personalized care through continuous patient monitoring and assessment of interventions using virtual models. However, realizing the complete potential of digital replicas in surgery faces obstacles such as data integration, interoperability, clinical applicability, and ethical concerns, demanding careful consideration. This paper explores the key elements of a distributed and reliable framework tailored for deploying DTs in precision surgery, while also identifying areas for further exploration and addressing unresolved issues. The insights presented aim to catalyze targeted innovation and foster interdisciplinary partnerships to effectively harness this emerging technology for healthcare transformation.

数字孪生（DT）技术通过启用支持数据驱动的诊断、治疗计划和预防护理的虚拟模型，为医疗保健行业提供了变革性的潜力。本文提出了一个强大的框架，旨在释放数字双胞胎在医疗保健服务各个方面的全部潜力。该过程从治疗前阶段开始，在此期间，来自多个来源的患者数据被集成以构建数字双胞胎模型，从而实现预测分析并帮助手术计划。在外科领域，DTs通过模拟手术过程、提供实时指导和促进医学专家之间的远程协作，证明了其宝贵价值。在术后阶段，他们通过使用虚拟模型对患者进行持续监测和干预评估来支持个性化护理。然而，在外科手术中实现数字复制品的全部潜力面临着诸如数据集成、互操作性、临床适用性和伦理问题等障碍，需要仔细考虑。本文探讨了分布式可靠框架的关键要素，用于在精确手术中部署dt，同时也确定了进一步探索和解决未解决问题的领域。提出的见解旨在促进有针对性的创新和促进跨学科合作伙伴关系，以有效地利用这一新兴技术进行医疗保健转型。

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引用次数: 0

Parkinson’s Disease detection through multimodal data analysis 通过多模态数据分析检测帕金森病

Q2 Health Professions

Smart Health

Pub Date : 2025-10-30 DOI: 10.1016/j.smhl.2025.100618

Arina Ivanova , Aleksei Shcherbak , Sergey Nesteruk , Ekaterina Bril , Anna Baldycheva , Andrey Somov

Parkinson’s disease (PD) is a slowly progressive neurodegenerative disease which still lacks objective tools for diagnosis. According to recent research results, misdiagnosis of PD may reach up to 25%. In this article, we report on the medical decision support system based on wearable sensors and video cameras with consequent “multimodal” data analysis using Machine Learning (ML) methods. For data collection reasons 169 subjects performed eleven exercises recommended by the neurologists. The proposed smart system is assessed through ML metrics and outperform the state-of-the-art solutions by achieving precision 98.6%, recall 98.1%, and F1-micro 98.3%. This decision support system opens wide vista for its application in hospitals as well as at home settings for controlling the undergoing therapy.

帕金森病（PD）是一种缓慢进展的神经退行性疾病，目前仍缺乏客观的诊断工具。根据最近的研究结果，PD的误诊率高达25%。在本文中，我们报告了基于可穿戴传感器和摄像机的医疗决策支持系统，以及随后使用机器学习（ML）方法的“多模态”数据分析。为了收集数据，169名受试者进行了神经科医生推荐的11项运动。提出的智能系统通过ML指标进行评估，并通过达到精度98.6%，召回率98.1%和F1-micro 98.3%而优于最先进的解决方案。该决策支持系统为其在医院以及家庭环境中的应用开辟了广阔的前景，以控制正在进行的治疗。

引用次数: 0

Glucose data interpretation in pediatric diabetes using an artificial intelligence approach 使用人工智能方法解释儿童糖尿病中的葡萄糖数据

Q2 Health Professions

Smart Health

Pub Date : 2025-10-11 DOI: 10.1016/j.smhl.2025.100616

Giovanni Paragliola , Sara Campanella , Valentino Cherubini , Valentina Tiberi , Paola Pierleoni , Alberto Belli , Antonio Iannilli , Lorenzo Palma

Semi-automatic solutions to monitor and treat diabetes have been recently developed, including insulin pumps and continuous glucose monitoring devices. Integrating computational techniques with electrical, communication, and information systems offers significant opportunities. However, no decision support systems are capable of adequately managing and analyzing the data provided by these devices. As a result, the high specificity and complexity of the information generated cannot be effectively utilized in everyday clinical practice. Therefore, this paper proposes an artificial-intelligent-based approach to identify distinct patterns within the glucose readings of pediatric diabetic patients. The objectives are twofold: first, to cluster the data employing a dimensionality reduction technique based on autoencoders, and second, to classify the data using the labels derived from the clustering phase to profile the glycemic trends. Furthermore, the blind evaluation conducted by medical professionals on the clustering results has offered crucial clinical validation to the work carried out. The results highlight the effectiveness and reliability of the proposed approach, achieving a classification performance with accuracy values up to 98%. The data reduction step was fundamental to speed up the subsequent processes while improving the metrics. The medical evaluation allowed us to improve the work by finding a correspondence between experimental results and clinical value.

最近开发了用于监测和治疗糖尿病的半自动解决方案，包括胰岛素泵和连续血糖监测装置。将计算技术与电气、通信和信息系统相结合提供了重要的机会。然而，没有决策支持系统能够充分管理和分析这些设备提供的数据。因此，所产生的信息的高特异性和复杂性无法在日常临床实践中得到有效利用。因此，本文提出了一种基于人工智能的方法来识别儿科糖尿病患者血糖读数中的不同模式。目标有两个：第一，使用基于自动编码器的降维技术对数据进行聚类，第二，使用从聚类阶段获得的标签对数据进行分类，以描绘血糖趋势。此外，医学专业人员对聚类结果进行的盲评价为所开展的工作提供了重要的临床验证。结果表明了该方法的有效性和可靠性，分类准确率高达98%。数据缩减步骤是加快后续过程的基础，同时改进度量标准。通过医学评估，我们找到了实验结果与临床价值的对应关系，从而改进了工作。

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引用次数: 0

Automatic and efficient micro univariate convolutional neural network framework for multiple neurological disorders from EEG signals 基于脑电信号的多种神经系统疾病的自动高效微单变量卷积神经网络框架

Q2 Health Professions

Smart Health

Pub Date : 2025-10-09 DOI: 10.1016/j.smhl.2025.100617

Shraddha Jain, Rajeev Srivastava, Sukomal Pal

Objective

Neurological disorders affect millions globally, contributing to significant morbidity and mortality. This study proposes the μCNN framework for enhanced EEG data analysis to improve diagnosis and classification of multiple neurological disorders.

Methods

The μCNN framework utilizes a unique 6-channel EEG spectrogram representation to extract key features from frequency and correlation components. The model uses convolutional layers, max-pooling, and batch normalization to classify disorders such as schizophrenia, Parkinson's, Alzheimer's, epilepsy, and stroke.

Results

On a dataset of 15,600 EEG spectrograms, the μCNN achieved 98.42 % accuracy, 98.4 % sensitivity, and 99.61 % specificity, outperforming ResNet50 and AlexNet in classification tasks.

Conclusions

The μCNN framework accurately categorizes neurological disorders with superior diagnostic accuracy and system performance.

Significance

This work advances EEG signal processing using deep learning, offering significant improvements in real-time diagnostic accuracy for a wide range of neurological disorders.

目的神经系统疾病影响着全球数百万人，造成了显著的发病率和死亡率。本研究提出了μCNN框架增强脑电图数据分析，以提高多种神经系统疾病的诊断和分类。方法μCNN框架利用独特的6通道脑电图表征，从频率和相关分量中提取关键特征。该模型使用卷积层、最大池化和批归一化来对精神分裂症、帕金森病、阿尔茨海默病、癫痫和中风等疾病进行分类。结果在15600张脑电图数据集上，μCNN的分类准确率为98.42%，灵敏度为98.4%，特异度为99.61%，优于ResNet50和AlexNet。结论μCNN框架能准确地对神经系统疾病进行分类，具有较高的诊断准确率和系统性能。这项工作推进了使用深度学习的脑电图信号处理，为广泛的神经系统疾病的实时诊断准确性提供了显着提高。

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引用次数: 0