Smart Health最新文献

{"title":"Amsel criteria based computer vision for diagnosing bacterial vaginosis","authors":"Daniel Highland,&nbsp;Gang Zhou","doi":"10.1016/j.smhl.2024.100501","DOIUrl":"10.1016/j.smhl.2024.100501","url":null,"abstract":"<div><p>Bacterial vaginosis (BV) is a common vaginal infection that can predispose patients to several complications, such as pelvic inflammatory disease. Like many illnesses, existing diagnostic methods face a trade-off between diagnostic certainty and cost. To help address this dilemma, we explore a computational diagnostic approach implementable as an IoT device. We developed several deep learning models based on the Amsel criteria to evaluate different inexpensive point-of-care tests that better automate the diagnosis of BV. We first determined how to best diagnose BV via computer vision models trained on epithelial cell images. We found that training a ResNet18 model on NuSwab diagnostic labels achieved an 89% F1 score. We then experimented with augmenting computer vision results with other Amsel criteria values through multi-layer perceptrons, finding that also using whiff test values increased performance to an F1 of 91% and to a sensitivity surpassing human-performed Amsel criteria at 94.31%. These results provide the first insight into how combinations of images and other Amsel criteria data can best be used for reliable diagnoses, paving the way for future research into IoT-based BV diagnostics.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100501"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850665","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":"Explainable SE-MobileNet for Pneumonia detection integrated with robustness assessment using adversarial examples","authors":"Somak Saha ,&nbsp;Chamak Saha ,&nbsp;Mohammad Zavid Parvez ,&nbsp;Md Tanzim Reza","doi":"10.1016/j.smhl.2024.100500","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100500","url":null,"abstract":"<div><p>Pneumonia is a detrimental disease, especially for children, which is caused due to bacterial infection. X-ray images are frequently observed manually to find out the existence of pneumonia in a patient’s body. However, diagnosing pneumonia using X-ray images through manual observation by different health professionals may lead to different conclusions. Thus, an efficient autonomic system is required to diagnose pneumonia from X-ray images, and deep learning techniques, such as CNN-based approaches are frequently used to create such autonomy. To ease the process of pneumonia diagnosis, in this study, we have proposed the SE-MobileNet approach. We compared the performance of the proposed SE-MobileNet with the default version of MobileNetV2 integrated with transfer learning. Using the publicly available Kaggle dataset, it is observed that SE-MobileNet obtained 97.4% accuracy on a select test set against the 96.4% accuracy of MobileNetV2, and in 10-fold cross-validation, SE-MobileNet achieved an average of 95.92% accuracy against the 92.35% accuracy of MobileNetV2. Further comparison analysis proves that the SE-MobileNet model not only performs much better than the vanilla MobileNetV2 but also performs competitively against the literature. In addition, robustness evaluation has been introduced in this study where Fast Gradient Sign Method (FGSM) is performed to generate adversarial images. It is found that in robustness evaluation, SE-MobileNet also performs better compared to MobileNetV2. Finally, to validate the appropriateness of the learning of the model, Explainable AI (XAI) based techniques have been employed.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100500"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141325545","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":"The impact of an AI-driven personal health platform on cardiovascular disease risk","authors":"Craig McNulty ,&nbsp;Justin Holland ,&nbsp;Cameron McDonald ,&nbsp;Marshall J. McGee","doi":"10.1016/j.smhl.2024.100499","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100499","url":null,"abstract":"<div><p>Cardiovascular disease (CVD) is a global health concern, particularly among older adults, linked to fat and sugar-rich diet, inactivity, tobacco smoking, as well as genetic factors. Regular exercise and a balanced diet are recommended to reduce CVD risk. Recently, the use of computer of mobile phone application platforms have become promenent tools for personal health interventions and guidance. Although research findings of the efficacy of mobile health apps has had mixed results, improved user health and lowered risk of CVD has been seen with regular engagement. Shae, an AI-driven mobile health platform, offers personalized lifestyle guidance based on an individual's phenotype. The aim of this study is to assess whether engagement with the online platform, Shae, decreases Framingham non-laboratory CVD risk score over time.</p><p>The study collected data from 1684 participants who engaged with the Shae online platform for at least 3-months between May 2014 and March 2018. After applying exclusion criteria, 5225 complete assessment entries remained for analysis. The Shae platform assessed health, predicted future risks, and recommended personalized interventions. Data included anthropometric measurements and survey responses, converted for standardized analysis. Descriptive statistics were calculated for participants' characteristics and compared across gender and age groups, in alignment with established cardiovascular disease (CVD) risk factors. A linear mixed-effect regression model was employed to assess changes in CVD risk over time. This research aimed to evaluate the impact of personalized health interventions on CVD risk in diverse demographic groups.</p><p>CVD risk score, when adjusted for age, decreased by 11.2% following 24 months of engagement with the Shae app, with men 45 years and older seeing the largest decrease (16.1%), and the lowest decrease seen in women under 55 years (10%). Use of the Shae app was primarily by women (85%).</p><p>This study assessed the impact of engagement with the mHealth app, Shae, on reducing CVD risk. Results revealed a significant CVD risk reduction, notably in men over 45. mHealth apps like Shae provide personalized health assessments, activity tracking, nutrition advice, and medication reminders, enabling proactive cardiovascular health management. Increased physical activity and better blood pressure control were key factors in risk reduction. Tailored interventions for high-risk groups align with previous research. Given the importance of preventing CVD, mHealth apps hold promise. This 24-month study offers robust insights, and the focus on individual phenotypes enhances engagement and health outcomes.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100499"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000552/pdfft?md5=f00ef3c52ea6e6893fd73aa926a9eafe&pid=1-s2.0-S2352648324000552-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141244888","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":"Semantic learning and attention dynamics for behavioral classification in police narratives","authors":"Dinesh Chowdary Attota,&nbsp;Abm Adnan Azmee,&nbsp;Md. Abdullah Al Hafiz Khan,&nbsp;Yong Pei,&nbsp;Dominic Thomas,&nbsp;Monica Nandan","doi":"10.1016/j.smhl.2024.100479","DOIUrl":"10.1016/j.smhl.2024.100479","url":null,"abstract":"<div><p>The proactive identification of behavioral health incidents concerns from police reports is a critical yet underexplored area. The law enforcement officers provide follow-up services to improve community life by manually analyzing and identifying generated public narrative reports after the 911 incident calls. Therefore, automatically identifying these behavioral health calls from public narrative reports helps reduce the current manual labor-intensive identification process for law enforcement officers. In this work, we introduce a novel, multi-faceted approach that combines manual expert annotations, natural language processing (NLP), and cutting-edge machine learning strategies to classify and understand these incidents within police narratives efficiently. Our proposed method retrieves relevant narrative reports utilizing domain knowledge as behavioral health cues as terms/keywords. Our approach automatically extracts different categories of behavioral health cues by utilizing the limited domain knowledge enabled behavioral health terms using a cosine similarity-based thresholding approach. The behavioral health classification model employs an automatic attention-aware feature representation of terms/keywords, categories, and narrative reports to identify behavioral health cases with an accuracy of 85%. Extensive evaluation shows that our proposed model outperforms all the state-of-the-art models by approximately 4%.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100479"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140276832","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":"Adapting goals and motivational messages on smartphones for motivation to walk","authors":"David Rei,&nbsp;Céline Clavel,&nbsp;Jean-Claude Martin,&nbsp;Brian Ravenet","doi":"10.1016/j.smhl.2024.100482","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100482","url":null,"abstract":"<div><p>Physical activity is one of the most recognised means of disease prevention. While several studies investigated different techniques for motivating people to walk (e.g. adaptive <em>goal setting</em>), only few studies considered user profiles to personalise interactions. In this article, we propose a new interaction model which adapts walking goals and motivational messages in a static way (using the initial physical activity level) and in a dynamic way (using previous days’ performance). We explain how we implemented this model in a mobile application counting and displaying in real time the number of steps, an adapted daily goal and personalised motivational messages throughout the day. We describe two field studies conducted with 32 and 50 users over four weeks. We compare the impacts of adapted daily goals and personalised motivational messages on users’ step counts and motivation to walk. Participants using the adapted version of the mobile application displayed an increase in their motivation to walk after the intervention and were more physically active than participants using non adapted versions of the mobile application.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100482"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140638111","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":"A smart demonstration unit for upper-limb myoelectric prostheses","authors":"Damiano Cosma Potenza ,&nbsp;Andrea Grazioso ,&nbsp;Federico Gaetani ,&nbsp;Giacomo Mantriota ,&nbsp;Giulio Reina","doi":"10.1016/j.smhl.2024.100481","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100481","url":null,"abstract":"<div><p>Demonstration unit devices are designed and built for the CPO (Certified Prosthetist Orthotist) to illustrate the functionalities of robotic hands to patients without the need for a prosthetic socket. Demonstration units are also of invaluable importance during the calibration and learning stages, which are preparatory for the final adoption of a prosthesis. This paper presents a smart demo unit (SDU) developed for Adam’s Hand, a robotic underactuated prosthesis proposed by BionIT Labs. It serves as a prosthetic sleeve that powers the robotic hand and gathers the electromyographic commands from the user. The details of the SDU are presented in terms of hardware and firmware, devoting special attention to the electromechanical interface with the robotic hand and the patient or CPO. The advantages of the system over existing alternatives are discussed, showing that the proposed SDU can be a valuable tool in the field of extremity upper limb prosthetics.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100481"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140638275","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":"EQS-Band Human Body Communication through frequency hopping and MCU-Based transmitter","authors":"Abdelhay Ali,&nbsp;Amr N. Abdelrahman,&nbsp;Abdulkadir Celik,&nbsp;Ahmed M. Eltawil","doi":"10.1016/j.smhl.2024.100471","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100471","url":null,"abstract":"<div><p>Human Body Communication (HBC) is an emerging technology that uses the human body as a communication channel. It offers significant advantages over traditional RF techniques in terms of power consumption and security. In recent developments, Electro-quasistatic HBC (EQS-HBC) in the frequency band below 1 MHz has been employed to enable communication without signal radiation beyond the body, effectively turning the body into a wired communication medium. This paper delves into the application of the EQS band for HBC. Experimental results show the determinantal effect of intermittent noise that sporadically disrupts communications across the band of interest. To address this challenge, we introduce an innovative frequency-hopping transceiver system, which allows the transmitter to seamlessly adapt to different frequencies. In addition, we present a miniature transmitter design, incorporating a simplified micro-controller unit (MCU) to facilitate the implementation of HBC. Furthermore, to validate this proposed design, we present a fully functional prototype of an HBC system that effectively employs frequency hopping techniques for practical applications.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100471"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140341331","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":"Few-shot meta-learning for pre-symptomatic detection of Covid-19 from limited health tracker data","authors":"Atifa Sarwar,&nbsp;Abdulsalam Almadani,&nbsp;Emmanuel O. Agu","doi":"10.1016/j.smhl.2024.100459","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100459","url":null,"abstract":"<div><p>Detecting (or screening for) Covid-19 even before symptoms fully manifest, could enable patients to receive timely and life-saving treatment. Prior work has demonstrated that heart rate and step data from low-end wearables analyzed using deep learning models can detect Covid-19 reliably. However, significant individual differences in vital sign manifestation (high inter-subject variability) present a challenge to the generalization of deep learning models across diverse users. The limited amount of data in many medical scenarios further exacerbates this issue. Consequently, neural network models that can learn from limited vital sign data and varied inter-subject patterns are compelling. Meta-learning has emerged as a powerful technique for tackling various machine learning challenges, including insufficient data, domain shifts across datasets, and issues with generalization. This study proposes <em>MetaCovid</em>, a deep adaptation framework that employs meta-learning to address the variability of vital sign manifestation between subjects using only two days of data in order to detect Covid-19 before symptoms manifest. <em>MetaCovid</em> leverages heart rate and step measurements collected from consumer-grade health trackers over the preceding 2 days, extracts 45 digital bio-markers (features), which along with raw data, are fed into a deep GRU-based network with an attention mechanism, followed by uncertainty filtering. <em>MetaCovid</em> is trained using OC-MAML, a one-class few-shot MAML variant that adapts to the target distribution/user using only samples from the majority class. <em>MetaCovid</em> generalized well across two relatively small, publicly available Covid-19 datasets, achieving a recall of 0.81 and 0.92, and detecting 61% (14 out of 23) and 50% (17 out of 34) of users infected with Covid-19 before symptom onset. When OC-MAML was excluded from <em>MetaCovid</em> in an ablation study, the F₂ score dropped by 36%, highlighting that meta-learning indeed facilitates adaptation of deep sensing models to varying vital sign patterns. Notably, <em>MetaCovid</em> outperforms the current state-of-art method by predicting Covid-19 early on day <span><math><mi>N</mi></math></span> using heart rate and step measurements from only the preceding 2 days compared to 28 days, reducing data requirements by 93%. To the best of our knowledge, our study is the first to propose utilizing meta-learning to mitigate vital sign variability with limited data for Covid-19 screening. We believe that <em>MetaCovid</em> will pave the way for innovative Covid-19 interventions that are accurate even with limited data and help contain the spread of infectious diseases in the future.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100459"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140031506","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":"Language-assisted deep learning for autistic behaviors recognition","authors":"Andong Deng ,&nbsp;Taojiannan Yang ,&nbsp;Chen Chen ,&nbsp;Qian Chen ,&nbsp;Leslie Neely ,&nbsp;Sakiko Oyama","doi":"10.1016/j.smhl.2023.100444","DOIUrl":"10.1016/j.smhl.2023.100444","url":null,"abstract":"<div><p>Correctly recognizing the behaviors of children with Autism Spectrum Disorder (ASD) is of vital importance for the diagnosis of Autism and timely early intervention. However, the observation and recording during the treatment from the parents of autistic children may not be accurate and objective. In such cases, automatic recognition systems based on computer vision and machine learning (in particular deep learning) technology can alleviate this issue to a large extent. Existing human action recognition models can now achieve impressive performance on challenging activity datasets, e.g., daily activity, and sports activity. However, problem behaviors in children with ASD are very different from these general activities, and recognizing these problem behaviors via computer vision is less studied. In this paper, we first evaluate a strong baseline for action recognition, i.e., Video Swin Transformer, on two autism behaviors datasets (SSBD and ESBD) and show that it can achieve high accuracy and outperform the previous methods by a large margin, demonstrating the feasibility of vision-based problem behaviors recognition. Moreover, we propose language-assisted training to further enhance the action recognition performance. Specifically, we develop a two-branch multimodal deep learning framework by incorporating the ”freely available” language description for each type of problem behavior. Experimental results demonstrate that incorporating additional language supervision can bring an obvious performance boost for the autism problem behaviors recognition task as compared to using the video information only (i.e., 3.49% improvement on ESBD and 1.46% on SSBD). <em>Our code and model will be publicly available for reproducing the results.</em></p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100444"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139016618","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":"IoT-based vital sign monitoring: A literature review","authors":"Alexandre Andrade,&nbsp;Arthur Tassinari Cabral,&nbsp;Bárbara Bellini,&nbsp;Vinicius Facco Rodrigues,&nbsp;Rodrigo da Rosa Righi,&nbsp;Cristiano André da Costa,&nbsp;Jorge Luis Victória Barbosa","doi":"10.1016/j.smhl.2024.100462","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100462","url":null,"abstract":"<div><p>The Internet of Things (IoT) applied to the health area is in significant growth, with companies putting effort into developing specialized devices. Remote patient healthcare monitoring, in particular, benefits society as it unburdens hospitals and helps patients with chronic diseases. Analyzing the health status with IoT and Artificial Intelligence (AI) is new in the digital community. The literature yet presents a limited number of references explicitly concerning the topic of qualified data acquisition. In this sense, the present literature review aims to update the joint subject of IoT and vital signs, seeking to understand the state-of-the-art and future directions. We have analyzed 78 articles and IoT manufacturer websites that address vital signs collection to answer a group of primary and specific questions. In particular, we revisited architectures, communication protocols, data acquisition mechanisms, evaluation metrics, and how to efficiently transfer data through the lens of sensors, actuators, and healthcare. Currently, two themes are considered as promising directions for studies in the joint area of IoT and vital sign-based healthcare monitoring. The first is the connection promotion between third-party applications and IoT devices to collect and process time-critical data with the support of edge, fog, and cloud infrastructures. The second theme again brings the focus to data compression methodologies since monitoring vital signs in a smart city geographical area naturally requires strategies to optimize network bandwidth consumption and data storage on computational resources. Moreover, both themes are directly linked to energy-saving approaches and quality of service (QoS) for efficient patient healthcare checking.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100462"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140195632","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}