Smart Health最新文献

{"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}

{"title":"Medication adherence management for in-home geriatric care with a companion robot and a wearable device","authors":"Fei Liang ,&nbsp;Zhidong Su ,&nbsp;Weihua Sheng ,&nbsp;Alex Bishop ,&nbsp;Barbara Carlson","doi":"10.1016/j.smhl.2023.100434","DOIUrl":"10.1016/j.smhl.2023.100434","url":null,"abstract":"<div><p><span>Older adults are prone to forgetfulness and varying degrees of cognitive impairment, which can lead to not taking medication on time, taking the wrong medication or the wrong dose, all of which can negatively affect a person’s health and recovery from illness. Existing medication reminders, like mobile apps and pill boxes, are neither age-friendly nor designed to minimize the burden of documenting medication adherence. In this paper, we present a Medication Adherence </span>Management System<span> (MAMS) for elders, which is based on a companion robot and a wearable device<span>. The MAMS addresses the key issues of safe medication management: medication reminders, medication confirmation, and medication history recording. Human subject tests were conducted to evaluate the performance, acceptability and usability of the MAMS. Results from 35 human subjects showed that the average scores of the convenience, usefulness, and adoptability of the proposed MAMS were 8.17, 8.49, and 8.23 out of 10, respectively. The System Usability Scale<span> (SUS) scores for the MAMS, the robot, and the wearable device are 75.29, 78.60 and 76.40, respectively. We believe the MAMS has potential use in future in-home geriatric care.</span></span></span></p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"30 ","pages":"Article 100434"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455637","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 smartphone accelerometer data-driven approach to recognize activities of daily life: A comparative study","authors":"Faisal Hussain ,&nbsp;Norberto Jorge Goncalves ,&nbsp;Daniel Alexandre ,&nbsp;Paulo Jorge Coelho ,&nbsp;Carlos Albuquerque ,&nbsp;Valderi Reis Quietinho Leithardt ,&nbsp;Ivan Miguel Pires","doi":"10.1016/j.smhl.2023.100432","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100432","url":null,"abstract":"<div><p>Smartphones have become an indispensable part of our everyday life, influencing various aspects of our routines, from wake-up alarms to managing daily life activities. Nowadays, almost every smartphone has a built-in accelerometer sensor. Motivated by the notable increase in smartphone usage in our everyday life, in this research, we focus on harnessing the potential of smartphone accelerometers to recognize human daily life activities, aiming to leverage the usability and convenience of smartphones. We used smartphone accelerometer data from data collection to daily life activity recognition. To accomplish this, we first collected the smartphone's accelerometer data while performing five activities of daily living (ADLs) namely: moving downstairs, upstairs, running, standing, and walking, from 25 volunteers through a mobile application. After this, we extracted 15 statistical features from the smartphone's accelerometer data to efficiently classify the five referred ADLs. We then applied data pre-processing techniques, i.e., data cleaning and feature extraction. Afterward, we trained nine commonly used machine learning models to recognize five ADLs. Finally, we evaluated and compared the performance of all nine ML models to recognize each activity and analyzed the performance of these trained ML models to identify all five ADLs. The evaluated results revealed that the Adaboost (AB) classifier outperformed all other ML models with 100% area under the curve (AUC), precision, recall, accuracy, and F1-score for recognizing the five ADLs.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"30 ","pages":"Article 100432"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648323000600/pdfft?md5=e8196ea22e3178380865a05fad79feca&pid=1-s2.0-S2352648323000600-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138087433","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":"Activity classification using unsupervised domain transfer from body worn sensors","authors":"Chaitra Hegde ,&nbsp;Gezheng Wen ,&nbsp;Layne C. Price","doi":"10.1016/j.smhl.2023.100431","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100431","url":null,"abstract":"<div><p>Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are emerging. To perform activity classification on a new body location, labeled data corresponding to the new locations are generally required, but this is expensive to acquire. In this work, we present an innovative method to leverage an existing activity classifier, trained on Inertial Measurement Unit (IMU) data from a reference body location (the source domain), in order to perform activity classification on a new body location (the target domain) in an unsupervised way, i.e. without the need for classification labels at the new location. Specifically, given an IMU embedding model trained to perform activity classification at the source domain, we train an embedding model to perform activity classification at the target domain by replicating the embeddings at the source domain. This is achieved using simultaneous IMU measurements at the source and target domains. The replicated embeddings at the target domain are used by a classification model that has previously been trained on the source domain to perform activity classification at the target domain. We have evaluated the proposed methods on three activity classification datasets PAMAP2, MHealth, and Opportunity, yielding high F1 scores of 67.19%, 70.40% and 68.34%, respectively when the source domain is the wrist and the target domain is the torso.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"30 ","pages":"Article 100431"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49734523","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}