Smart Health最新文献

{"title":"Application of time series analysis to classify therapeutic breathing patterns","authors":"João Lucas Oliveira Canhoto ,&nbsp;Paulo Salgado Gomes de Mattos Neto ,&nbsp;Taiwan Roberto Barbosa ,&nbsp;José Emmanuel Matias da Silva Santos ,&nbsp;Igor Mauricio de Campos ,&nbsp;Geraldo Leite Maia Junior ,&nbsp;João Victor Cordeiro Coutinho ,&nbsp;Márcio Evaristo da Cruz Brito ,&nbsp;Anna Luisa Araújo Brito ,&nbsp;Daniella Cunha Brandão ,&nbsp;Armele de Fátima Dornelas de Andrade ,&nbsp;Herbert Albérico de Sá Leitão ,&nbsp;Shirley Lima Campos","doi":"10.1016/j.smhl.2024.100460","DOIUrl":"10.1016/j.smhl.2024.100460","url":null,"abstract":"<div><h3>Objective</h3><p>Compare various methods for measuring time series similarity in order to classify referenced therapeutic breathing patterns (BP) used in respiratory disorder rehabilitation.</p></div><div><h3>Methods</h3><p>This experimental study involved the collection of respiratory signals during specified breathing exercises conducted with healthy volunteers. The study employed a screening phase using a k-NN classifier and eight distance measurement methods, including Minkowski Distance, Dynamic Time Warping-DTW (including FastDTW and constrained-cDTW variations), Longest Common Subsequence-LCSS, Edit Distance on Real Sequences-EDR, Time Warp Edit Distance-TWEED, and Minimum Jump Costs-MJC. Two distinct approaches were employed for classifying therapeutic BP based on time series similarity: (1) using the k-Shape algorithm for clustering, and 2) integrating methods to represent therapeutic BP and classify test curves using the most relevant measurement methods obtained from the first approach.</p></div><div><h3>Results</h3><p>Among the two tested approaches, the combination of the cDTW algorithm and Minkowski distance (p = 2), using the 1-NN classifier, achieved the highest scores in this study, closely matching the metrics obtained from visual inspection conducted by human evaluators.</p></div><div><h3>Conclusion</h3><p>The use of combined classification methods in the analysis of flow curves referring to therapeutic breathing patterns improves the classification results, with metrics closely aligned with those obtained through visual evaluation conducted by individuals.</p></div><div><h3>Significance</h3><p>Time series analysis methods proved to be sensitive to classify respiratory flow curves equivalent to therapeutic breathing patterns used in respiratory disorder rehabilitation. This methodology can be used to monitor respiratory curves in new applications and implementation in devices for evaluating and treating the ventilatory pattern.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100460"},"PeriodicalIF":0.0,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091152","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":"2024-02-27","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":"Retraction notice to ‘Blockchain technology in the future of healthcare’ [Smart Health 23 (2022) 100223]","authors":"Quazi Mamun","doi":"10.1016/j.smhl.2024.100458","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100458","url":null,"abstract":"","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100458"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235264832400014X/pdfft?md5=fac559044334cbd8c0b8378ca18a3a66&pid=1-s2.0-S235264832400014X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139999226","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":"Recent trends and techniques of blood glucose level prediction for diabetes control","authors":"Benzir Md. Ahmed ,&nbsp;Mohammed Eunus Ali ,&nbsp;Mohammad Mehedy Masud ,&nbsp;Mahmuda Naznin","doi":"10.1016/j.smhl.2024.100457","DOIUrl":"10.1016/j.smhl.2024.100457","url":null,"abstract":"<div><p>Diabetes, a metabolic disorder disease, can cause short-term acute or even long-term chronic complications in a patient’s body. In 2021, 10.5% of the world’s adult population had diabetes. These numbers are increasing day by day, which results in an associated increase of morbidity, mortality, and health care cost related to diabetes. Thus, a huge research effort has been carried out to manage diabetes. A precursor to diabetes management is to predict the future blood glucose levels based on a patient’s past history. In this paper, we provide a comprehensive and systematic study of diabetes management, focusing on recent research towards blood glucose level prediction. In particular, we have categorized and presented existing recent research based on major clinical application domains, different input features, and major modeling techniques including physiological, data-driven, and hybrid models. We have summarized the performance analysis of different modeling techniques using different metrics, and critically analyzed these techniques from different perspectives. Finally, we have identified a number of research challenges and potential future works that range from data collection to model improvement for Type 2 Diabetes Mellitus. This review can be a good starting point for researchers and practitioners who are working in building data-driven computational models for diabetes management and blood glucose level prediction.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100457"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139880929","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":"Recent trends and techniques of blood glucose level prediction for diabetes control","authors":"B. M. Ahmed, Mohammed Eunus Ali, Mohammad Mehedy Masud, Mahmuda Naznin","doi":"10.1016/j.smhl.2024.100457","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100457","url":null,"abstract":"","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"551 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139820696","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":"Design and technical evaluation of an AMBU-BAG based low-cost ventilator-AARMED","authors":"Mohit Kumar ,&nbsp;Ravinder Kumar ,&nbsp;Vishal Kumar ,&nbsp;Amanpreet Chander ,&nbsp;Abhinav Airan ,&nbsp;Rajesh Arya ,&nbsp;Gurpreet Singh Wander ,&nbsp;Ashish Kumar Sahani","doi":"10.1016/j.smhl.2023.100445","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100445","url":null,"abstract":"<div><p>The COVID-19 pandemic has caused a significant strain on the healthcare system worldwide, resulting in an acute shortage of ventilators. Conventional ventilators are costly, and production is difficult to scale up during a rapidly spreading pandemic. Ambu bags offer a low-cost solution for manual ventilation, but their lack of precise control over parameters makes them unsuitable as a replacement for conventional ventilators. To address this issue, we propose the AARMED (Ambu bag Attachment for Rapid Mass Emergency Deployment) system, which is a low-cost and easy-to-assemble mechanical resuscitator that can achieve most of the recommended modes and parameter ranges for managing COVID-19 patients. AARMED can operate in volume-control, pressure-control, and assist-controlled modes continuously over long periods, making it suitable for use in emergency settings. The AARMED system has been tested using an ISO certified Test-lung over various parameter settings and has been found to be an effective alternative to costly conventional ventilators. It has a battery backup of 2.5 h under normal operating conditions, making it an ideal transport ventilator. In conclusion, the AARMED system offers a low-cost and easy-to-assemble solution for mechanical ventilation in emergency settings. Its ability to achieve most of the recommended modes and parameter ranges for managing COVID-19 patients makes it a viable alternative to conventional ventilators in resource-constrained settings.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"31 ","pages":"Article 100445"},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648323000739/pdfft?md5=687d1e050cacbb7658c4f24834b63b29&pid=1-s2.0-S2352648323000739-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504670","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":"CardioHelp: A smartphone application for beat-by-beat ECG signal analysis for real-time cardiac disease detection using edge-computing AI classifiers","authors":"Ucchwas Talukder Utsha,&nbsp;Bashir I. Morshed","doi":"10.1016/j.smhl.2024.100446","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100446","url":null,"abstract":"<div><p>Cardiovascular diseases are a leading cause of morbidity and mortality worldwide. To diagnose cardiac diseases, physicians often utilize a combination of medical history, physical examination, and several diagnostic tests, such as electrocardiograms (ECG/EKG), echocardiograms, and stress tests. Early detection and effective management of cardiac diseases play a crucial role in improving patient outcomes and reducing healthcare burden. To address this concern, we introduce a novel edge-computing approach for cardiac healthcare using a smartphone application (CardioHelp) that combines heart rate monitoring with the detection of abnormal heartbeats in individuals. Our approach centers around a user-friendly smart-health application designed to visualize ECG signals, track and monitor heart rate continuously, and recognize and notify users of any anomalies through advanced beat-by-beat ECG analysis algorithms and artificial intelligence (AI) techniques including machine learning and deep learning. Our system includes a custom wearable ECG data collection system that can transfer data to CardioHelp in real-time. In this study, we have used the MIT-BIH Arrhythmia dataset to train deep learning models using intricate patterns and features representative of various heart conditions. Among the deep learning models, the Long Short-Term Memory (LSTM) demonstrated superior performance, obtaining an accuracy of 98.74% and precision and recall of 99.95% and 99.86%, respectively. By transferring the MIT-BIH Arrhythmia Database’s test dataset through our application as mock real-time data, we assessed our CardioHelp application’s accuracy in identifying and classifying various heart conditions. The LSTM model is found to be the most accurate model providing an accuracy of 95.94% for ECG beat classification. The results confirmed the effectiveness of our developed smartphone system, demonstrating its ability to accurately detect and classify cardiac conditions. As our novel approach presents a complimentary cardiac healthcare system using a smart health application, this CardioHelp has the potential to significantly enhance preventive care, enable early intervention, and improve overall cardiovascular health outcomes.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"31 ","pages":"Article 100446"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000011/pdfft?md5=9c56788fa103efe37a12332fec7595c8&pid=1-s2.0-S2352648324000011-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139493933","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":"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":"2023-12-22","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":"Behavioural intention of mobile health adoption: A study of older adults presenting to the emergency department","authors":"Mathew Aranha ,&nbsp;Jonah Shemie ,&nbsp;Kirstyn James ,&nbsp;Conor Deasy ,&nbsp;Ciara Heavin","doi":"10.1016/j.smhl.2023.100435","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100435","url":null,"abstract":"<div><h3>Background</h3><p>The COVID-19 pandemic highlighted the challenges of providing quality healthcare to vulnerable populations, especially older adults who are disproportionately affected by health service disruptions. Increasingly, mobile health (mHealth) is used for remote healthcare service delivery in this group; however, a variety of factors may limit its adoption.</p></div><div><h3>Aims</h3><p>To identify the prevalence of mobile device usage among older adults (65yrs+) who present to acute hospitals and explore their willingness to use mHealth.</p></div><div><h3>Methods</h3><p>A cross-sectional study was conducted using convenience sampling to recruit adults over 65 years to complete a 28 question, 5-point-Likert questionnaire developed using the Unified Theory of Acceptance and Use of Technology (UTAUT).</p></div><div><h3>Results</h3><p>This study included 119 older adults. Fifty-three participants (44.5%) did not own a smartphone, and 53 (44.5%) had never used one. Sixty-six participants (55.5%) indicated an intention to use mHealth while 53 (44.5%) were either ambivalent or had no intention to use it. Smartphone owners were significantly more likely to use mHealth (OR:3.27, CI:1.53–6.95) than non-owners. Participants showed high self-efficacy (median = 4.0) and expected mHealth to perform well (median = 3.67) with minimal effort (median = 3.33). Within this cohort, intention to use is predicted by age (β = 0.163, p = 0.03), performance expectancy (β = 0.329, p = 0.01), effort expectancy (β = 0.231, p = 0.01) and subjective health status (β = −0.171, p = 0.01).</p></div><div><h3>Conclusions</h3><p>Many older adults attending acute hospitals remain disinclined in mHealth. This is associated with minimal experience to mobile devices. Empowering older adults to benefit from the increasingly digital landscape of healthcare will require uncovering creative ways to engage them in programs that increase their use of mHealth services.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"31 ","pages":"Article 100435"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648323000636/pdfft?md5=c961e5fb99f6594c1d2110dbae135fd0&pid=1-s2.0-S2352648323000636-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138466796","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":"Pregnancy healthcare monitoring system: A review","authors":"Nasim Khozouie ,&nbsp;Razieh Malekhoseini","doi":"10.1016/j.smhl.2023.100433","DOIUrl":"10.1016/j.smhl.2023.100433","url":null,"abstract":"<div><p>Today's the blend of information technology and medicine has been improved patient's life. People can monitor their health status without the aid of a healthcare specialized; healthcare is now ubiquitous and don't limit in the waiting room. Unfortunately, there are rarely worked done on women's healthcare, especially pregnancy healthcare monitoring. In this research, a number of articles that have been specially presented about new measurement systems for daily life and health monitoring systems for pregnant women are investigated. Then, a separate overview of these research was presented based on the type of device used and an explanation of their structure. Finally, a model was designed and proposed to test comprehensive systems for monitoring the health of pregnant women. The proposed model is designed based on wearable and environmental sensors that collect daily medical data from pregnant women.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"31 ","pages":"Article 100433"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648323000612/pdfft?md5=38974d36c33c47e4431b0d5a9db0da50&pid=1-s2.0-S2352648323000612-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135669358","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}