Design and Development of a Comprehensive and Interactive Diabetic Parameter Monitoring System - BeticTrack

A novel, interactive Android app has been developed that monitors the health of type 2 diabetic patients in real-time, providing patients and their physicians with real-time feedback on all relevant parameters of diabetes. The app includes modules for recording carbohydrate intake and blood glucose; for reminding patients about the need to take medications on schedule; and for tracking physical activity, using movement data via Bluetooth from a pair of wearable insole devices. Two machine learning models were developed to detect seven physical activities: sitting, standing, walking, running, stair ascent, stair descent and use of elliptical trainers. The SVM and decision tree models produced an average accuracy of 85% for these seven activities. The decision tree model is implemented in an app that classifies human activity in real-time

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Future Opportunities for IoT to Support People with Parkinson’s

Recent years have seen an explosion of internet of things (IoT) technologies being released to the market. There has also been an emerging interest in the potentials of IoT devices to support people with chronic health conditions. In this paper, we describe the results of engagements to scope the future potentials of IoT for supporting people with Parkinson’s. We ran a 2-day multi-disciplinary event with professionals with expertise in Parkinson’s and IoT, to explore the opportunities, challenges and benefits. We then ran 4 workshops, engaging 13 people with Parkinson’s and caregivers, to scope out the needs, values and desires that the community has for utilizing IoT to monitor their symptoms. This work contributes a set of considerations for future IoT solutions that might support people with Parkinson’s in better understanding their condition, through the provision of objective measurements that correspond to their, currently unmeasured, subjective experiences

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Monitoring of medication boxes using wireless sensors

Medication adherence is a real problem among older adults which can lead to serious repercussions on their health and life. Adherence is defined by the World Health Organization as the extent to which the behavior of a person corresponds with recommendations from a health care provider. A low medication adherence to a certain prescription can undermine the treatment benefits in many cases. Moreover, taking wrong medication may lead to unwanted secondary effects, adverse health conditions, and visits to the hospital. This dissertation describes the work focused on the design, development, and research of a solution for monitoring medication boxes using attached sensors. The main contributions of this work include the development of a mobile application, a study on how to classify data from medication box gestures, an implementation of the algorithm that retrieves data from sensor boxes, and an integration of the data classification algorithm into the mobile application. A medication reminder proof-of-concept was developed in the scope of this Master’s project. Sensor data is received by the prototype through a module that integrates the connection and data transference from the sensor boxes via wireless communication. Another module implements metric extraction functions that are applied to the inertial sensor data retrieved from the sensor box. The calculated metrics, herein corresponding to features, are passed to a machine learning algorithm, integrated in the data classification and feature extraction module, for posterior data identification. An in-depth analysis on how to classify inertial data from medication box gestures was conducted during the development of the solution. This in-depth analysis included the creation of two datasets with different characteristics which were preprocessed and fed to several machine learning algorithms. The analysis of the results outputted by the algorithms is included in this document. The dataset collection took place in two different locations, corresponding to a controlled environment and to a non-controlled environment. The obtained results showed that it is possible to identify the gestures in the dataset for the controlled environment, with the best results achieving a true positive rate of 97:9%. The results obtained for the dataset of the non-controlled environment (which was created with target users) showed that there are still many aspects that need to be improved before a final version of the solution is released.Uma baixa adesão à terapêutica é um problema real entre os adultos que pode levar a sérias repercussões nas suas vidas. A adesão à terapêutica é definida pela Organização Mundial de Saúde como a medida em que o comportamento de uma pessoa coincide com as recomendações de um prestador de cuidados de saúde. Uma baixa adesão a uma determinada terapêutica pode comprometer, em muitos casos, os benefícios do tratamento. Além disso, tomar medicação errada pode levar a efeitos secundários não desejados, condições de saúde adversas e visitas a hospitais. Esta dissertação descreve um trabalho focado na concepção, desenvolvimento e investigação de uma solução para a monitorização de caixas de medicação com caixas de sensores a elas acopladas. As principais contribuições deste trabalho incluem o desenvolvimento de uma aplicação móvel, um estudo em como classificar dados de gestos de caixas de medicação, uma implementação do algoritmo que obtém dados das caixas de sensores e a integração do algoritmo de aprendizagem automática na aplicação móvel. Foi desenvolvida uma prova-de-conceito de alarmes de medicação no âmbito deste projecto de Mestrado. Os dados dos sensores são recebidos pelo protótipo através de um módulo que integra a ligação e transferência de dados das caixas de sensores via ligação sem fios. Outro módulo implementa funções de extração de métricas que serão usadas sobre os dados dos sensores inerciais contidos nas caixas de sensores. As métricas calculadas, também chamadas de características, são passadas para um algoritmo de aprendizagem automática, que está integrado no módulo de classificação de dados e extração de características, para posterior identificação de dados. No desenvolvimento da solução, foi feito um estudo aprofundado sobre como classificar dados inerciais de gestos de caixas de medicação. Este estudo incluiu a criação de dois conjuntos de dados com diferentes características que, depois de serem pré-processados, foram submetidos a diferentes algoritmos de aprendizagem automática, sendo os seus resultados analisados neste documento. O processo de coleção de dados foi feito em dois locais distintos, correspondendo a um ambiente controlado e um ambiente não controlado. Os resultados obtidos mostram que é possível identificar os gestos considerados no ambiente controlado, tendo os melhores resultados chegado a 97:9% de taxa de acerto. Os resultados obtidos para o conjunto de dados do ambiente não controlado (que contou com a participação dos utilizadores alvo da aplicação) demonstraram que ainda há aspetos a melhorar antes de produzir uma versão final da solução

Implementation of the IoT-Based Technology on Patient Medication Adherence: A Comprehensive Bibliometric and Systematic Review

The dynamic field of the Internet of Things (IoT) is constantly increasing, providing a plethora of potential integration across various sectors, most notably healthcare. The IoT represents a significant technological leap in healthcare management systems, coinciding with the rising preference for personalized, proactive, cost-effective treatment techniques. This review aimed to thoroughly assess the existing literature through a systematic review and bibliometric analysis, identifying untapped research routes and possible domains for further exploration. The overarching goal was to provide healthcare professionals with significant insights into the impact of IoT technology on Patient Medication Adherence (PMA) and related outcomes. An extensive review of 314 scientific articles on the deployment of IoT within pharmaceutical care services revealed a rising trend in publication volume, with a significant increase in recent years. Pertinently, from the 33 publications finally selected, substantial data support the potential of the IoT to improve PMA, particularly among senior patients with chronic conditions. This paper also comments on various regularly implemented IoT-based systems, noting their unique benefits and limitations. In conclusion, the critical relevance of PMA is highlighted, arguing for its emphasis in future discussions. Furthermore, the need for additional research endeavors is proposed to face and overcome existing constraints and establish the long-term effectiveness of IoT technologies in maximizing patient outcomes

A Comparison of Machine Learning Gesture Recognition Techniques for Medication Adherence

Every year, many poor health outcomes are the result of patients missing their medication, as prescribed by their healthcare providers. Guidance and reminders to these patients would result in better health outcomes and significant financial savings to the economy. This thesis utilizes accelerometers and gyroscopes, which are widely available inside devices (e.g., smart phones and watches) to actively monitor patient activities, including those related to adherence to medication regimens. Different machine learning techniques are compared for recognizing when a pill bottle has been opened. Such actions could remind the patient to take their medication if an opening were not detected. An artificial neural network (ANN) model will be compared with a support vector machine (SVM) and a K-nearest neighbor (KNN) classifier. The models are trained on data collected by former University of Oklahoma students. Raw (normalized) sensor data is used, without extensive data processing or feature extraction. A neural network proves the most promising with an accuracy of 98.12%, as well as the greatest flexibility in data pre-processing requirements. KNN achieved high accuracy, although results were likely due to overfitting limited data with the simple model. SVM did not perform as well as the others, however; it did achieve similar results to previous research utilizing the approach (e.g., ~95% accuracy). Data collected from a greater number of gestures and additional test subjects is needed to verify generalization. A medication adherence system utilizing the developed model would be an acceptable approach

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