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

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

Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP). Methods: Thirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback. Results: Adherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score > 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = −0.560, BCa 95% CI [−0.791, −0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS. Conclusion: This study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP

Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP). Methods: Thirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback. Results: Adherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score > 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = −0.560, BCa 95% CI [−0.791, −0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS. Conclusion: This study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject