Smart Health最新文献

{"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}

{"title":"DeepaMed: Deep learning-based medication adherence of Parkinson's disease using smartphone gait analysis","authors":"Hamza Abujrida,&nbsp;Emmanuel Agu,&nbsp;Kaveh Pahlavan","doi":"10.1016/j.smhl.2023.100430","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100430","url":null,"abstract":"<div><h3>Objectives</h3><p>Parkinson's disease (PD) is a neurodegenerative chronic disorder with multiple motor and non-motor symptoms. As PD has no ultimate cure, physicians aim to delay PD complications, especially those that degrade the patient's quality of life such as motor symptoms and dyskinesia. Patients' lack of adherence to prescribed medication is a major challenge for physicians, especially for patients suffering from chronic conditions. The Centers for Disease Control and Prevention (CDC) estimates that medication non-adherence causes 30 to 50 percent of chronic disease treatment failures and 125,000 deaths per year in the USA (U.S. Foods and Drugs Administration (FDA) “Why You Need to Take Your Medications as Prescribed or Instructed” <span>https://www.fda.gov/drugs/special-features/why-you-need-take-your-medications-prescribed-or-instructed</span><svg><path></path></svg>. June 2021). In PD patients particularly, adherence varies between 10% and 67% (Straka et al., 2019Straka, Igor, et al. \"Adherence to pharmacotherapy in patients with Parkinson's disease taking three and more daily doses of medication.\" Frontiers in neurology 10 (2019): 799).</p></div><div><h3>Objective</h3><p>The goal of this work is to remotely determine whether PD patients have taken their medication, by analyzing gait data gathered from their smartphone sensors. Using this approach, physicians can track the level of medication adherence of their PD patients.</p></div><div><h3>Methodology</h3><p>Using data from the mPower study (Bot et al., 2016), we selected 152 PD patients who recorded at least 3 walks before and 3 after taking medications and 304 healthy controls (HC) who recorded 3 walks at minimum. We extracted each subject's gait cycle from their accelerometer and gyroscope sensors data. The sensor data corresponding to gait cycles were fed to DeePaMed; a multilayer Conventional Neural Network (CNN), crafted for patches of gait strides. DeePaMed classified 30 s of a walk as either PD patient “On” vs. “Off” medication, or if the gait data belongs to an HC.</p></div><div><h3>Results</h3><p>Our DeePaMed model was able to discriminate PD patients on-vs off-medication and baseline HC walk with an accuracy of <strong>98.2%</strong>. The accuracy of our CNN model surpassed that of traditional Machine Learning methods by over <strong>17%</strong>. We also found that our model performed best with inputs containing a minimum of 10 full gait strides.</p></div><div><h3>Conclusion</h3><p>Medication non-adherence can be accurately predicted using smartphone sensing of the motor symptoms of PD, suggesting that PD patients’ medication response and non-adherence can be monitored remotely via smartphone-based measures.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"30 ","pages":"Article 100430"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49716778","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":"Personalized diabetes monitoring platform leveraging IoMT and AI for non-invasive estimation","authors":"Durga Padmavilochanan ,&nbsp;Rahul Krishnan Pathinarupothi ,&nbsp;K.A. Unnikrishna Menon ,&nbsp;Harish Kumar ,&nbsp;Ramesh Guntha ,&nbsp;Maneesha V. Ramesh ,&nbsp;P. Venkat Rangan","doi":"10.1016/j.smhl.2023.100428","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100428","url":null,"abstract":"<div><p>Non-invasive blood glucose estimation is an extensively researched area since current gold-standard invasive glucose monitoring methods present numerous inconveniences and challenges in terms of comfort and cost. We present the design, development, and validation of an Internet of Medical Things (IoMT) based wearable device for non-invasive and real-time measurement of blood glucose. This paper presents a diabetic health monitoring platform architecture that consists of (a) a user-worn photoplethysmography (PPG) device, (b) a smart analytics cloud that deploys models for blood glucose estimation, and (c) an end-to-end mobile/web application for monitoring diabetes patients. Blood glucose computation is achieved using a novel light-weight 1-dimensional input-reinforced deep neural network architecture, which we call as GlucoNet. This captures both long and short, temporal and spatial features from the PPG signal. The training and validation of the model were conducted on a dataset of 283 participants which demonstrated a mean absolute percentage error (MAPE) of 17.8% (<span><math><mo>±</mo></math></span> 12.8%) wherein 100% of predictions fall in the clinically acceptable zones A and B of the Clarke-error grid. The lightweight model is also deployed on edge devices for real-time and offline blood glucose measurement. We report a clinical outcome deployment study and insights from 20,000＋ glucose measurements obtained from another 600 patients. To our knowledge, this is the largest reported work employing a non-calibrated, non-invasive, demography, and time-of-food agnostic IoMT glucose monitoring system that does not require any feature engineering and is capable of running on edge devices.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"30 ","pages":"Article 100428"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49734519","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 AI for malnutrition risk prediction from m-Health and clinical data","authors":"Flavio Di Martino ,&nbsp;Franca Delmastro ,&nbsp;Cristina Dolciotti","doi":"10.1016/j.smhl.2023.100429","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100429","url":null,"abstract":"<div><p>Malnutrition is a serious and prevalent health problem in the older population, and especially in hospitalised or institutionalised subjects. Accurate and early risk detection is essential for malnutrition management and prevention. M-health services empowered with Artificial Intelligence (AI) may lead to important improvements in terms of a more automatic, objective, and continuous monitoring and assessment. Moreover, the latest Explainable AI (XAI) methodologies may make AI decisions interpretable and trustworthy for end users.</p><p>This paper presents a novel AI framework for early and explainable malnutrition risk detection based on heterogeneous m-health data. We performed an extensive model evaluation including both subject-independent and personalised predictions, and the obtained results indicate Random Forest (RF) and Gradient Boosting as the best performing classifiers, especially when incorporating body composition assessment data. We also investigated several benchmark XAI methods to extract global model explanations. Model-specific explanation consistency assessment indicates that each selected model privileges similar subsets of the most relevant predictors, with the highest agreement shown between SHapley Additive ExPlanations (SHAP) and feature permutation method. Furthermore, we performed a preliminary clinical validation to verify that the learned feature-output trends are compliant with the current evidence-based assessment.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"30 ","pages":"Article 100429"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49716775","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":"An end-to-end authentication mechanism for Wireless Body Area Networks","authors":"Mosarrat Jahan,&nbsp;Fatema Tuz Zohra,&nbsp;Md. Kamal Parvez,&nbsp;Upama Kabir,&nbsp;Abdul Mohaimen Al Radi,&nbsp;Shaily Kabir","doi":"10.1016/j.smhl.2023.100413","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100413","url":null,"abstract":"<div><p>Wireless Body Area Network (WBAN) ensures a high-quality healthcare service to patients by providing remote and relentless monitoring of their health conditions. Nevertheless, the patients’ health-related data are very sensitive and require security and privacy while transmitting through WBAN to maximize its benefit. User authentication is one of the primary mechanisms to protect critical data, which verifies the identities of entities involved in data transmission. Hence, in the case of health data, every entity engaged in the data transfer process over WBAN needs to be authenticated. In the literature, an end-to-end user authentication mechanism covering each communicating party must be included. Besides, most of the existing user authentication mechanisms are designed assuming that the patient’s mobile phone is trusted. However, a patient’s mobile phone can be stolen or compromised by various malware, therefore, can behave maliciously. To address these limitations, this paper proposes an end-to-end user authentication and session key agreement scheme between sensors and medical experts where the patient’s mobile phone is semi-trusted. We present a formal security analysis using BAN logic and an informal security analysis of the proposed scheme. Both studies reveal that the proposed methodology is robust against well-known security attacks. We analyze the performance of the proposed scheme by collecting real data in practical deployments and find that our scheme achieves comparable efficiency in computation, communication, and energy usage overheads concerning state-of-the-art methods. Besides, the NS-3 simulation exhibits that our proposed scheme also preserves a satisfactory network performance.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"29 ","pages":"Article 100413"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49741888","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":"Application of systems modeling language (SysML) and discrete event simulation to address patient waiting time issues in healthcare","authors":"Niamat Ullah Ibne Hossain ,&nbsp;Mostafa Lutfi ,&nbsp;Ifaz Ahmed ,&nbsp;Hunter Debusk","doi":"10.1016/j.smhl.2023.100403","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100403","url":null,"abstract":"<div><p>A robust health care system is crucial to reducing patient stress and contributing to economic growth. The future of the health care industry depends upon a reliable and efficient system to deal with the increasing number of patients. However, in today's healthcare system, patients face negative experiences as a result of long wait times. Now the pressing question is how to develop an effective healthcare system? To address this issue, this study uses Systems Modeling Language (SysML) coupled with a simulation approach to assess the performance of the healthcare system, identify the problem, and offer recommended alternatives. To elaborate, a systemic magic-grid methodology will be used to model and analyze the blood laboratory by using four pillars (structural, behavioral, requirement, and parametric) of SysML. To represent these pillars, a set of SysML diagrams will be used to visualize the layered system architecture, interactions, and activity between its different components. Furthermore, Discrete Event Simulation (DES) is utilized through Flexsim simulation software for the analysis of the parametric aspect of the system of interest. A blood laboratory within an outpatient clinic located at southern US State is considered a testing bed. The detailed architecture of the system of interest is studied, and required data are collected for modeling and simulation. The simulation results indicate that the combination of 50% Type I routes and 50% Type II routes resulted in the shortest wait times in the system of 22 min, the shortest wait times in the phlebotomist queue of 2 min, and the highest system throughput of 11369 patients per nine months. This article will provide a reference point for practitioners who want to apply the SysML approach to address health sector-related issues. More importantly, with this comprehensive approach, stakeholders of the blood laboratory system can utilize the hospital infrastructure in a more effective and optimized manner.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"29 ","pages":"Article 100403"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49741889","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":"Toward personalized rehabilitation employing classification, localization, and visualization of brain–arm movement relationships","authors":"Soroush Korivand, Xishi Zhu, N. Jalili, Kyung Koh, Li-Qun Zhang, Jiaqi Gong","doi":"10.1016/j.smhl.2023.100397","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100397","url":null,"abstract":"","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46216525","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":"Short:VANet: An Intuitive Light-Weight Deep Learning Solution Towards Ventricular Arrhythmia Detection","authors":"Tianyu Chen,&nbsp;Alexander Gherardi,&nbsp;Anarghya Das,&nbsp;Huining Li,&nbsp;Chenhan Xu,&nbsp;Wenyao Xu","doi":"10.1016/j.smhl.2023.100388","DOIUrl":"https://doi.org/10.1016/j.smhl.2023.100388","url":null,"abstract":"<div><p>Ventricular Arrhythmia (VA) is a leading cause of sudden cardiac death (SCD), which kills an average of 180,000 to 350,000 people annually, accounting for 15%–20% of all deaths. Furthermore, fewer than 6% of those who experience sudden cardiac arrest outside the hospital survive, compared to 24% of those who experience SCD inside a hospital. To aid in earlier detection and improve outcomes for out-of-hospital cardiac events, an automated passive detection system for these events could be used. Such automated detection would allow users to raise their self-awareness of potential cardiac risks in life-threatening situations. Diagnosis and detection of heart dysfunctions at early stages can help to prevent complications of a patient’s condition.</p><p>In this work, we propose VANet and design framework for ECG-related application, a small-scale deep learning-based real-time inference solution for VA detection. VANet achieves milliseconds scale inference speed on various platforms, including desktop CPUs, mobile devices, micro-controllers, and devices with constrained computation resources. It only requires a minimum of 13 kb of storage space and 34 kb of available run-time, making it small enough to be integrated into portable devices such as smartwatches and other Internet of Things (IoT) medical monitoring devices. VANet can trigger an alarm whenever it is necessary to alert someone with cardiac dysfunction.</p><p>VANet leverages optimization techniques, such as residual connections, and architecture designs, such as transformers and RNNs, to maximize neural network performance and minimize computational and storage costs. Our architecture achieved a 96.89% accuracy using multiple different ECG collection devices.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"28 ","pages":"Article 100388"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49717075","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}