Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Chronic diseases are a growing concern worldwide,with nearly 25% of adults suffering from one or more chronic health conditions,thus placing a heavy burden on individuals,families,and healthcare systems.With the advent of the“Smart Healthcare”era,a series of cutting-edge technologies has brought new experiences to the management of chronic diseases.Among them,smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state.However,how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management,in terms of quality of life,patient outcomes,and privacy protection,is an urgent issue that needs to be addressed.Artificial intelligence(AI)can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of diseases.In addition,blockchain can improve healthcare services by authorizing decentralized data sharing,protecting the privacy of users,providing data empowerment,and ensuring the reliability of data management.Integrating AI,blockchain,and wearable technology could optimize the existing chronic disease management models,with a shift from a hospital-centered model to a patient-centered one.In this paper,we conceptually demonstrate a patient-centric technical framework based on AI,blockchain,and wearable technology and further explore the application of these integrated technologies in chronic disease management.Finally,the shortcomings of this new paradigm and future research directions are also discussed.

Emerging wearable technology applications in gastroenterology:A review of the literature

The field of gastroenterology has recently seen a surge in wearable technology to monitor physical activity,sleep quality,pain,and even gut activity.The past decade has seen the emergence of wearable devices including Fitbit,Apple Watch,AbStats,and ingestible sensors.In this review,we discuss current and future devices designed to measure sweat biomarkers,steps taken,sleep efficiency,gastric electrical activity,stomach pH,and intestinal contents.We also summarize several clinical studies to better understand wearable devices so that we may assess their potential benefit in improving healthcare while also weighing the challenges that must be addressed.

Measuring Pedestrian Stress Response (MPSR) Using Wearable Technologies

Walkability is an essential aspect of urban transportation systems. Properly designed walking paths can enhance transportation safety, encourage pedestrian activity, and improve community quality of life. This, in turn, can help achieve sustainable development goals in urban areas. This pilot study uses wearable technology data to present a new method for measuring pedestrian stress in urban environments and the results were presented as an interactive geographic information system map to support risk-informed decision-making. The approach involves analyzing data from wearable devices using heart rate variability (RMSSD and slope analysis) to identify high-stress locations. This data-driven approach can help urban planners and safety experts identify and address pedestrian stressors, ultimately creating safer, more walkable cities. The study addresses a significant challenge in pedestrian safety by providing insights into factors and locations that trigger stress in pedestrians. During the pilot study, high-stress pedestrian experiences were identified due to issues like pedestrian-scooter interaction on pedestrian paths, pedestrian behavior around high foot traffic areas, and poor visibility at pedestrian crossings due to inadequate lighting.

A randomized controlled trial of Promoting Physical Activity in Regional and Remote Cancer Survivors(PPARCS)

Background:Physical activity(PA)is important for cancer survivors.Trials of remotely delivered interventions are needed to assist in reaching under-served non-metropolitan cancer survivors.The objective of this study was to ascertain whether wearable technology,coupled with health coaching was effective in increasing PA in breast and colorectal cancer survivors living in regional and remote areas in Australia.Methods:Cancer survivors from 5 states were randomized to intervention and control arms.Intervention participants were given a Fitbit Charge 2TMand received up to 6 telephone health coaching sessions.Control participants received PA print materials.Accelerometer assessments at baseline and 12 weeks measured moderate-to-vigorous PA(MVPA),light PA,and sedentary behavior.Results:Eighty-seven participants were recruited(age=63±11 years;74(85%)female).There was a significant net improvement in MVPA of 49.8 min/week,favoring the intervention group(95%confidence interval(95%CI):13.6-86.1,p=0.007).There was also a net increase in MVPA bouts of 39.5 min/week(95%CI:11.9-67.1,p=0.005),favoring the intervention group.Both groups improved light PA and sedentary behavior,but there were no between-group differences.Conclusion:This’s the first study to demonstrate that,when compared to standard practice(i.e.,PA education),a wearable technology intervention coupled with distance-based health coaching,improves MVPA in non-metropolitan cancer survivors.The results display promise for the use of scalable interventions using smart wearable technology in conjunction with phone-based health coaching to foster increased PA in geographically disadvantaged cancer survivors.

Use of technology and social media in dementia care:Current and future directions

As the population across the globe continues to dramatically increase,the prevalence of cognitive impairment and dementia will inevitably increase as well,placing increasing burden on families and health care systems.Technological advancements over the past decade provide potential benefit in not only relieving caregiver burden of caring for a loved one with dementia,but also enables individuals with dementia to age in place.Technological devices have served to improve functioning,tracking and mobility.Similarly,smartphones,tablets and the ubiquitous world wide web have facilitated the dissemination of health information to previously hard to reach populations largely through use of various social media platforms.In this review,we discuss the current and future uses of technology via devices and social media to promote healthy aging in individuals with dementia,and also limitations and challenges to consider in the future.

Privacy-preserving deep learning techniques for wearable sensor-based big data applications

Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)applications.Wearable technology uses electronic devices that may be carried as accessories,clothes,or even embedded in the user's body.Although the potential benefits of smart wearables are numerous,their extensive and continual usage creates several privacy concerns and tricky information security challenges.In this paper,we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors.We highlight the fundamental features of security and privacy for wearable device applications.Then,we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors.We also present a case study on privacy-preserving machine learning techniques.Herein,we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance.We explain the implementation details of a case study of a secure prediction service using the convolutional neural network(CNN)model and the Cheon-Kim-Kim-Song(CHKS)homomorphic encryption algorithm.Finally,we explore the obstacles and gaps in the deployment of practical real-world applications.Following a comprehensive overview,we identify the most important obstacles that must be overcome and discuss some interesting future research directions.

The personal protective equipment(PPE)based on individual combat:A systematic review and trend analysis

With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce casualties and maintain military combat effectiveness,is widely developed.This paper systematically reviewed various PPE based on individual combat through literature research and comprehensive discussion,and introduced in detail the latest application progress of PPE in terms of material and technology from three aspects:individual integrated protection system,traditional protection equipment,and intelligent protection equipment,respectively,and discussed in depth the functional improvement and optimization status brought by advanced technology for PPE,focusing on the achievements of individual equipment technology application.Finally,the problems and technical bottlenecks in the development of PPE were analyzed and summarized,and the development trend of PPE were pointed out.The results of the review will provide a forward-looking reference for the current development of individual PPE,and are important guidance for the design and technological innovation of advanced equipment based on the future technological battlefield.

Wearable sensing,big data technology for cardiovascular healthcare:current status and future prospective

Wearable technology,which can continuously and remotely monitor physiological and behavioral parameters by incorporated into clothing or worn as an accessory,introduces a new era for ubiquitous health care.With big data technology,wearable data can be analyzed to help long-term cardiovascular care.This review summarizes the recent developments of wearable technology related to cardiovascular care,highlighting the most common wearable devices and their accuracy.We also examined the application of these devices in cardiovascular healthcare,such as the early detection of arrhythmias,measuring blood pressure,and detecting prevalent diabetes.We provide an overview of the challenges that hinder the widespread application of wearable devices,such as inadequate device accuracy,data redundancy,concerns associated with data security,and lack of meaningful criteria,and offer potential solutions.Finally,the future research direction for cardiovascular care using wearable devices is discussed.

Traffic Priority-Aware Medical Data Dissemination Scheme for IoT Based WBASN Healthcare Applications

Wireless Body Area Sensor Network(WBASN)is an automated system for remote health monitoring of patients.WBASN under umbrella of Internet of Things(IoT)is comprised of small Biomedical Sensor Nodes(BSNs)that can communicate with each other without human involvement.These BSNs can be placed on human body or inside the skin of the patients to regularly monitor their vital signs.The BSNs generate critical data as it is related to patient’s health.The data traffic can be classified as Sensitive Data(SD)and Non-sensitive Data(ND)packets based on the value of vital signs.These data packets have different priority to deliver.The ND packets may tolerate some delay or packet loss whereas,the SD packets required to be delivered on time with minimized packet loss otherwise it can be life threating to the patients.In this research,we propose a Traffic Priority-aware Medical Data Dissemination(TPMD2)scheme forWBASN to deliver the data packets according to their priority based on the sensitivity of the data.The assessment of the proposed scheme is carried out in various experiments.The simulation results of the TPMD2 scheme indicate a significant improvement in packets delivery,transmission delay and energy efficiency in comparison with the existing schemes.

Machine learning model for snow depth estimation using a multisensory ubiquitous platform

Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction.Despite advances in remote sensing technology and enhanced satellite observations,the estimation of snow depth at local scale still requires improved accuracy and flexibility.The advances in ubiquitous and wearable technology promote new prospects in tackling this challenge.In this paper,a wearable IoT platform that exploits pressure and acoustic sensor readings to estimate and classify snow depth classes using some machine-learning models have been put forward.Significantly,the results of Random Forest classifier showed an accuracy of 94%,indicating a promising alternative in snow depth measurement compared to in situ,LiDAR,or expensive large-scale wireless sensor network,which may foster the development of further affordable ecological monitoring systems based on cheap ubiquitous sensors.

Improving the Ambient Intelligence Living Using Deep Learning Classifier

Over the last decade,there is a surge of attention in establishing ambient assisted living(AAL)solutions to assist individuals live independently.With a social and economic perspective,the demographic shift toward an elderly population has brought new challenges to today’s society.AAL can offer a variety of solutions for increasing people’s quality of life,allowing them to live healthier and more independently for longer.In this paper,we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks(BiLSTM)and convolutional neural network(CNN)classifier.We first pre-processed the signal data,then used timefrequency features such as signal energy,signal variance,signal frequency,empirical mode,and empirical mode decomposition.The convolutional neural network-bidirectional long-term and short-term memory(CNN-biLSTM)classifier with dimensional reduction isomap algorithm was then used to select ideal features.We assessed the performance of our proposed system on the publicly accessible human gait database(HuGaDB)benchmark dataset and achieved an accuracy rates of 93.95 percent,respectively.Experiments reveal that hybrid method gives more accuracy than single classifier in AAL model.The suggested system can assists persons with impairments,assisting carers and medical personnel.