The rise of consumer health wearables: promises and barriers

Will consumer wearable technology ever be adopted or accepted by the medical community? Patients and practitioners regularly use digital technology (e.g., thermometers and glucose monitors) to identify and discuss symptoms. In addition, a third of general practitioners in the United Kingdom report that patients arrive with suggestions for treatment based on online search results. However, consumer health wearables are predicted to become the next “Dr Google.” One in six (15%) consumers in the United States currently uses wearable technology, including smartwatches or fitness bands. While 19 million fitness devices are likely to be sold this year, that number is predicted to grow to 110 million in 2018. As the line between consumer health wearables and medical devices begins to blur, it is now possible for a single wearable device to monitor a range of medical risk factors. Potentially, these devices could give patients direct access to personal analytics that can contribute to their health, facilitate preventive care, and aid in the management of ongoing illness. However, how this new wearable technology might best serve medicine remains unclea

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Use of the Smartphone Camera to Monitor Adherence to Inhaled Therapy

Self-management strategies can lead to improved health outcomes, fewer unscheduled treatments, and improved disease control. Compliance with inhaled control drugs is essential to achieve good clinical outcomes in patients with chronic respiratory diseases. However, compliance assessments suffer from the difficulty of achieving a high degree of trustworthiness, as patients often self-report high compliance rates and are considered unreliable. This thesis aims to enable reliable adhesion measurement by developing a mobile application module to objectively verify inhalation usage using image snapshots of the inhalation counter. To achieve this, a mobile application module featuring pre and post processing techniques and a default machine learning framework was built, for inhaler and dosage counter numbers detection. In addition, in an effort to improve the app’s capabilities of text recognition on a worst-performing inhaler, a machine learning model was trained on an inhaler image dataset. Some of the features worked on during this project were incorporated on the current version of the app InspirerMundi, a medication management mobile application, planned to be made available at the PlayStore by the end of 2021. The proposed approach was validated through a series of different inhaler image datasets. The carried-out tests with the default machine learning configuration showed correct detection of dosage counters for 70% of inhaler registration events and 93% for three commonly used inhalers in Portugal. On the other hand, the trained model had an average accuracy of 88 % in recognizing the digits on the dose counter of one of the worst-performing inhaler models. These results show the potential to explore mobile and embedded capabilities to gain additional evidence for inhaler compliance. These systems can help bridge the gap between patients and healthcare professionals. By empowering patients with disease selfmanagement and drug adherence tools and providing additional relevant data, these systems pave the way for informed disease management decisions

Wiki-health: from quantified self to self-understanding

Today, healthcare providers are experiencing explosive growth in data, and medical imaging represents a significant portion of that data. Meanwhile, the pervasive use of mobile phones and the rising adoption of sensing devices, enabling people to collect data independently at any time or place is leading to a torrent of sensor data. The scale and richness of the sensor data currently being collected and analysed is rapidly growing. The key challenges that we will be facing are how to effectively manage and make use of this abundance of easily-generated and diverse health data. This thesis investigates the challenges posed by the explosive growth of available healthcare data and proposes a number of potential solutions to the problem. As a result, a big data service platform, named Wiki-Health, is presented to provide a unified solution for collecting, storing, tagging, retrieving, searching and analysing personal health sensor data. Additionally, it allows users to reuse and remix data, along with analysis results and analysis models, to make health-related knowledge discovery more available to individual users on a massive scale. To tackle the challenge of efficiently managing the high volume and diversity of big data, Wiki-Health introduces a hybrid data storage approach capable of storing structured, semi-structured and unstructured sensor data and sensor metadata separately. A multi-tier cloud storage system—CACSS has been developed and serves as a component for the Wiki-Health platform, allowing it to manage the storage of unstructured data and semi-structured data, such as medical imaging files. CACSS has enabled comprehensive features such as global data de-duplication, performance-awareness and data caching services. The design of such a hybrid approach allows Wiki-Health to potentially handle heterogeneous formats of sensor data. To evaluate the proposed approach, we have developed an ECG-based health monitoring service and a virtual sensing service on top of the Wiki-Health platform. The two services demonstrate the feasibility and potential of using the Wiki-Health framework to enable better utilisation and comprehension of the vast amounts of sensor data available from different sources, and both show significant potential for real-world applications.Open Acces

A survey on wireless body area networks for eHealthcare systems in residential environments

The progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to the base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments

The safe administration of medication within the electromagnetic scenarios of the Internet of Things (IoT): looking towards the future

This paper has focused on analyzing the impact of Information and Communication Technologies (ICTs) to prevent or reduce errors during therapeutic drug administration. The methodology used has included scientific literature and marketed appliances reviews and laboratory tests on radiant devices. The role of the patient has been analyzed, both in terms of compliance with the prescribed treatments and user of technical solutions designed for administering medication. In addition, it has taken into account, how a future characterized by multiple technologies designed to support our daily routines, including health care, might affect the current model of relationship between health professionals and patients. Particular attention has been given to safety risks of ICTs in environments characterized by concurrent electromagnetic emissions operating at different frequencies. Implications and new scenarios from Internet of Things or IoT, have been considered, in light of the approach taken jointly by the European Commission and the European Technology Platform on Intelligent Systems Integration – EPoSS, in their 2008 report Internet of Things in 2020: a roadmap for the future, and how the concept has evolved since then.Chapter 1. Adverse drug events. Chapter 2. ICTs in everyday life and healthcare. Chapter 3. the challenge of electromagnetic safety. Chapter 4. ICTs in health care and in the prevention of medication errors: IoT. Chapter 5. A more effective and safer alternative approach. Chapter 6. Technological proposal 7. Conclusions.N

Human-centred artificial intelligence for mobile health sensing:challenges and opportunities

Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions