Recent breakthrough technological progressions of powerful mobile computing resources such as low-cost mobile GPUs along with cutting-edge, open-source software architectures have enabled high-performance deep learning on mobile platforms. These advancements have revolutionized the capabilities of today's mobile applications in different dimensions to perform data-driven intelligence locally, particularly for smart health applications. Unlike traditional machine learning (ML) architectures, modern on-device deep learning frameworks are proficient in utilizing computing resources in mobile platforms seamlessly, in terms of producing highly accurate results in less inference time. However, on the flip side, energy resources in a mobile device are typically limited. Hence, whenever a complex Deep Neural Network (DNN) architecture is fed into the on-device deep learning framework, while it achieves high prediction accuracy (and performance), it also urges huge energy demands during the runtime. Therefore, managing these resources efficiently within the spectrum of performance and energy efficiency is the newest challenge for any mobile application featuring data-driven intelligence beyond experimental evaluations. In this paper, first, we provide a timely review of recent advancements in on-device deep learning while empirically evaluating the performance metrics of current state-of-the-art ML architectures and conventional ML approaches with the emphasis given on energy characteristics by deploying them on a smart health application. With that, we are introducing a new framework through an energy-aware, adaptive model comprehension and realization (EAMCR) approach that can be utilized to make more robust and efficient inference decisions based on the available computing/energy resources in the mobile device during the runtime.
Deakin University（迪肯大学）；Pusan National University（釜山国立大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；University of New South Wales（新南威尔士大学）；The University of Sydney（悉尼大学）；Memorial University（纪念大学）
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing …
SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction
School of Computer Science, Peking University（北京大学计算机学院）；National Research and Engineering Center of Software Engineering, Peking University（北京大学国家软件工程研究与工程中心）；Center on Frontiers of Computing Studies, Peking University（北京大学计算前沿研究中心）
United Arab Emirates University（阿联酋大学）；The University of Melbourne（墨尔本大学）
Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life …
University College Dublin（都柏林大学学院）；School of Computer Science（计算机学院）
Food-choices and eating-habits directly contribute to our long-term health. This makes the food recommender system a potential tool to address the global crisis of obesity and malnutrition. Over the past decade, artificial-intelligence and medical researchers became more invested in researching tools that can guide and help people make healthy and thoughtful decisions around food and diet. In many typical (Recommender System) RS domains, smart nudges have been proven effective in shaping users' consumption patterns. In recent years, knowledgeable nudging and incentifying choices started gettin…
The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These smart health monitoring systems could be realized in a smart home context to render long-term care to the elderly population. In this paper, we present the design of a wearable health monitoring system suitable for older adults in a smart home context. The proposed system offers solutions to monitor the stress, blood pressure, and location of an individual within a smart home environment. The stress detection model proposed in this work uses Electrodermal Activity (EDA), Photoplethysmogr…
SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning
University of Melbourne（墨尔本大学）；United Arab Emirates University（阿联酋大学）；Emirates Center for Mobility Research（阿联酋交通研究中心）；National Water and Energy Center（国家水资源与能源中心）；UAE University（阿联酋大学）
Daffodil International University（达法迪尔国际大学）；University of Calgary（卡尔加里大学）
Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, like using video captured by the surveillance cameras or depth image cameras in the lab environment. It also can be recognized by wearable sensors. e.g., accelerometer, force sensors, gyroscope, flexible goniometer, magneto resistive sensors, electromagnetic tracking system, force s…
Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Harvard Medical School（哈佛医学院）；Harvard Pilgrim Health Care Institute（哈佛 pilgrim 健康护理研究所）
Background: Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarize the existing approaches to smartphone-based HAR. Methods: We systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones f…
The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart heal…
University of Quebec（魁北克大学）；ETS（高等技术学院）；Princeton University（普林斯顿大学）
Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centralized training approach of artificial intelligence (AI), the use of mobile and wearable IoMT devices raises privacy concerns with respect to the information that has been communicated between hospitals and end users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm has ope…
A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting using Smartphone Sensing Data
Singapore University of Technology and Design（新加坡科技设计大学）；Tianqiao and Chrissy Chen Institute（田桥和克里斯西·陈研究所）；Nanyang Technological University（南洋理工大学）；Purdue University（普渡大学）
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
Imperial College London（伦敦帝国学院）；Massachusetts General Hospital（麻省总医院）；Zhejiang University（浙江大学）；Weill Cornell Medicine of Cornell University（康奈尔大学威尔医学院）；Royal Brompton Hospital（皇家布朗普顿医院）；King’s College London（伦敦国王学院）
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare
Dr. B. C. Roy Engineering College（B.C.罗伊工程学院）；Bengal College of Engg. and Tech.（孟加拉工程技术学院）；National Institute of Technology（印度国家理工学院）；University of North Texas（北得克萨斯大学）
本文提出 Easydiagnos 框架，通过融合遗传算法、可解释 AI 和排列组合技术的 AFE 算法优化医疗特征选择，并在多个数据集上验证其可提升临床决策的准确性与可解释性。
Indian Institute of Technology (Banaras Hindu University) Varanasi（瓦拉纳西印度理工学院（贝拿勒斯印度教大学））
This paper proposes Federated Learning (FL) based smart healthcare system where Medical Centers (MCs) train the local model using the data collected from patients and send the model weights to the miners in a blockchain-based robust framework without sharing raw data, keeping privacy preservation into deliberation. We formulate an optimization problem by maximizing the utility and minimizing the loss function considering energy consumption and FL process delay of MCs for learning effective models on distributed healthcare data underlying a blockchain-based framework. We propose a solution in t…
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data
An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices,…
University of Oxford（牛津大学）；Roche Innovation Center（罗氏创新中心）；KU Leuven（鲁汶大学）
The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. In this work, deep convolutional neural networks (DCNN) applied to smartphone inertial sensor data were shown to better distinguish healthy from MS participant ambulation, compared to standard Support Vector Machine (SVM) feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, …
The increasing availability of healthcare data requires accurate analysis of disease diagnosis, progression, and realtime monitoring to provide improved treatments to the patients. In this context, Machine Learning (ML) models are used to extract valuable features and insights from high-dimensional and heterogeneous healthcare data to detect different diseases and patient activities in a Smart Healthcare System (SHS). However, recent researches show that ML models used in different application domains are vulnerable to adversarial attacks. In this paper, we introduce a new type of adversarial …
SmartAlert: Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Lab Utilization Reduction
Stanford University School of Medicine（斯坦福大学医学院）；Stanford University（斯坦福大学）；SmarterDx；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Stanford Health Care（斯坦福医疗保健）；Stanford Health Care Tri-Valley（斯坦福医疗保健三谷医院）
Tennessee Tech University（田纳西理工大学）；California State San Bernardino（加州州立大学圣贝纳迪诺分校）；HBKU（哈马德·本·哈利法大学）；Florida International University（佛罗里达国际大学）
Electronic health records (EHR) systems contain vast amounts of medical information about patients. These data can be used to train machine learning models that can predict health status, as well as to help prevent future diseases or disabilities. However, getting patients' medical data to obtain well-trained machine learning models is a challenging task. This is because sharing the patients' medical records is prohibited by law in most countries due to patients privacy concerns. In this paper, we tackle this problem by sharing the models instead of the original sensitive data by using the mim…
Implementing a Nordic-Baltic Federated Health Data Network: a case report
Norwegian Centre for E-health Research（挪威电子健康研究中心）；UiT The Arctic University of Norway（挪威北极大学）；DNV（挪威船级社）；University of Eastern Finland（东芬兰大学）；University of Turku（图尔库大学）；University of Tartu（塔尔图大学）；Linköping University（林雪平大学）
Mental health problems impact quality of life of millions of people around the world. However, diagnosis of mental health disorders is a challenging problem that often relies on self-reporting by patients about their behavioral patterns. Therefore, there is a need for new strategies for diagnosis of mental health problems. The recent introduction of body-area networks consisting of a plethora of accurate sensors embedded in smartwatches and smartphones and deep neural networks (DNNs) points towards a possible solution. However, disease diagnosis based on WMSs and DNNs, and their deployment on …
Vellore Institute of Technology Chennai（韦洛尔理工学院金奈分校）
In todays world of smart living, the smart laptop bag, presented in this paper, provides a better solution to keep track of our precious possessions and monitoring them in real time. As the world moves towards a much tech-savvy direction, the novel laptop bag discussed here facilitates the user to perform location tracking, ambiance monitoring, user-state monitoring etc. in one device. The innovative design uses cloud computing and machine learning algorithms to monitor the health of the user and many parameters of the bag. The emergency alert system in this bag could be trained to send approp…
Dublin City University（都柏林城市大学）；HealthBeacon Ltd（HealthBeacon有限公司）
Medication non-adherence is a widespread problem affecting over 50% of people who have chronic illness and need chronic treatment. Non-adherence exacerbates health risks and drives significant increases in treatment costs. In order to address these challenges, the importance of predicting patients' adherence has been recognised. In other words, it is important to improve the efficiency of interventions of the current healthcare system by prioritizing resources to the patients who are most likely to be non-adherent. Our objective in this work is to make predictions regarding individual patients…
University of Jeddah（吉达大学）；Menoufia University（米努菲亚大学）；University of Bradford（布拉德福德大学）；Sejong University（世宗大学）；Misr Higher Institute of Commerce and Computers（米斯尔高等商业与计算机学院）；Nile University（尼罗河大学）；Harbin Institute of Technology（哈尔滨工业大学）
. In this paper, an effective computer-aided diagnosis (CAD) system is presented to detect MI signals using the convolution neural network (CNN) for urban healthcare in smart cities. Two types of transfer learning techniques are employed to retrain the pre-trained VGG-Net (Fine-tuning and VGG-Net as fixed feature extractor) and obtained two new networks VGG-MI1 and VGG-MI2. In the VGG-MI1 model, the last layer of the VGG-Net model is replaced with a specific layer according to our requirements and various functions are optimized to reduce overfitting. In the VGG-MI2 model, one layer of the VGG…
