Sensor Drive Mobile application for health awareness The SSURE (Software System for User Running Evaluation) app for Android: a design that won’t let you down

There has been a significant increase in the number of mobile applications concerned with health awareness due to the increase in the number of people who are concerned about their health and the raise in the number of people using smartphone/tablet devices. The development of applications related to health and exercise has become popular both in industry and academia. In this project we focus on development of a mobile application that can capture a user’s running movement and modify an audio file so that there is a synchronisation between the beats of the music and the kinetic data (cadence or Steps per Minute/SPM) to motivate and guide their exercise. Our approach applies time-frequency analysis to obtain the SPM value by using the Lomb Periodogram technique that can effectively process unevenly sampling data, which is a feature of the data captured from the built-in accelerometer sensor on a smartphone/tablet device. In order to process the time-stretched audio file that is adjusted with the running information, the Phase Vocoder technique was used to transform the sound to different speed without changing the pitch. Its sophisticated frequency-domain sound processing suits our project’s objective. To guide the implementation of these algorithms, several Software Engineering techniques have been used to manage our project. The Agile Development Lifecycle (SDLC) technique known as SCRUM was used throughout the development process in the design, testing, and implementation phases. This technique allowed us to change the plan if it was necessary, so it suited our project which was dealing with a new technology to be implemented within a short and limited timespan. Finally, we presented our evaluation to determine the accuracy of the results from our approaches and to assess the quality of our application. The results of evaluation showed that our approaches for the functional requirements were effective and gave us accurate response. However the non-functional requirements still needed to be improved and it was found that a new mobile-oriented approach for software metrics is needed if we wanted to achieve our goals fully

Towards AI-assisted Healthcare: System Design and Deployment for Machine Learning based Clinical Decision Support

Over the last decade, American hospitals have adopted electronic health records (EHRs) widely. In the next decade, incorporating EHRs with clinical decision support (CDS) together into the process of medicine has the potential to change the way medicine has been practiced and advance the quality of patient care. It is a unique opportunity for machine learning (ML), with its ability to process massive datasets beyond the scope of human capability, to provide new clinical insights that aid physicians in planning and delivering care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction. However, applying ML-based CDS has to face steep system and application challenges. No open platform is there to support ML and domain experts to develop, deploy, and monitor ML-based CDS; and no end-to-end solution is available for machine learning algorithms to consume heterogenous EHRs and deliver CDS in real-time. Build ML-based CDS from scratch can be expensive and time-consuming. In this dissertation, CDS-Stack, an open cloud-based platform, is introduced to help ML practitioners to deploy ML-based CDS into healthcare practice. The CDS-Stack integrates various components into the infrastructure for the development, deployment, and monitoring of the ML-based CDS. It provides an ETL engine to transform heterogenous EHRs, either historical or online, into a common data model (CDM) in parallel so that ML algorithms can directly consume health data for training or prediction. It introduces both pull and push-based online CDS pipelines to deliver CDS in real-time. The CDS-Stack has been adopted by Johns Hopkins Medical Institute (JHMI) to deliver a sepsis early warning score since November 2017 and begins to show promising results. Furthermore, we believe CDS-Stack can be extended to outpatients too. A case study of outpatient CDS has been conducted which utilizes smartphones and machine learning to quantify the severity of Parkinson disease. In this study, a mobile Parkinson disease severity score (mPDS) is generated using a novel machine learning approach. The results show it can detect response to dopaminergic therapy, correlate strongly with traditional rating scales, and capture intraday symptom fluctuation

Otimização de rotas num sistema de Bike-Sharing usando sinais fisiológicos

Com o passar dos anos, o bike-sharing tem merecido uma crescente atenção, devido às iniciativas para aumentar o uso de bicicletas em detrimento dos transportes motorizados. O objetivo desta mudança visa minimizar o uso dos transportes urbanos que libertam gases poluentes causando problemas ambientais e congestionamento de tráfego. Tendo em vista a alternativa aos transportes urbanos e veículos motorizados, com objetivo de adotar um estilo de vida verde e saudável, várias cidades em todo o planeta tentam proporcionar esse estilo de vida aos seus cidadãos, construindo sistemas os bike-sharing e pondo à disposição parques de estacionamento com bicicletas.Neste documento, pretende-se mencionar a criação de um sistema que com a medição de parâmetros físicos, através de sensores fisiológicos e dispositivos Wearable, pode dar a perceber quais as rotas mais otimizadas para um utilizador e de que forma a utilização dos sistema de bike-sharing se pode articular com os restantes transportes públicos da cidade. No final da dissertação, espera-se como resultado uma aplicação Android, denominada SmartBikeEmotion, que pretende permitir aos utilizadores usufruir do que os sistemas de bike-sharing proporcionam, com o acréscimo da adaptação dos circuitos à sua condição física, auxiliando na sua movimentação pela cidade. Esta aplicação será integrada com o projeto BikeEmotion, um projeto de bike-sharing, desenvolvido em consórcio pela Ubiwhere, Ponto.C, Micro I/O, e Universidade de Aveiro.Over the years, the bike-sharing has received a widespread attention due to initiatives to increase the use of bicycles instead of motorized transports. The objective of this change is to minimize the use of urban transport that release greenhouse gases which causes environmental problems and traffic congestion. Take in account the alternative to urban transport and motor vehicles, in order to adopt a green and healthy lifestyle, many cities around the world attempts to provide this lifestyle to its citizens, building bike-sharing systems and making available parking spaces for bicycles.In this paper, we intended to show the creation of a system which with the measurement of physical parameters, through physiological sensors and Wearable devices, may advise about the more optimized routes for the users and how the bike-sharing system can be linked with other public transports. In the end of this thesis, we expect an Android application, called SmartBikeEmotion, which aims to allow the users take advantages of the bike-sharing system, with the strength of the circuits adaption to the user's physical condition, assisting them in the cycling through the city. This application will be integrated with BikeEmotion project, a bike-sharing project, developed in partnership by Ubiwhere, Ponto.C, Micro I / O, and University of Aveiro

Mechanisms for improving information quality in smartphone crowdsensing systems

Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs. Existing smartphone sensing systems depend completely on the participants\u27 willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the information quality of the sensing reports submitted by the participants. To this end, mechanisms to improve information quality in smartphone crowdsensing systems were designed in this work. Firstly, the FIRST framework is presented, which is a reputation-based mechanism that leverages the concept of mobile trusted participants to determine and improve the information quality of collected data. Secondly, it is mathematically modeled and studied the problem of maximizing the likelihood of successful execution of sensing tasks when participants having uncertain mobility execute sensing tasks. Two incentive mechanisms based on game and auction theory are then proposed to efficiently and scalably solve such problem. Experimental results demonstrate that the mechanisms developed in this thesis outperform existing state of the art in improving information quality in smartphone crowdsensing systems --Abstract, page iii

Otimização de rotas num sistema de Bike-Sharing usando sinais fisiológicos

Com o passar dos anos, o bike-sharing tem merecido uma crescente atenção, devido às iniciativas para aumentar o uso de bicicletas em detrimento dos transportes motorizados. O objetivo desta mudança visa minimizar o uso dos transportes urbanos que libertam gases poluentes causando problemas ambientais e congestionamento de tráfego. Tendo em vista a alternativa aos transportes urbanos e veículos motorizados, com objetivo de adotar um estilo de vida verde e saudável, várias cidades em todo o planeta tentam proporcionar esse estilo de vida aos seus cidadãos, construindo sistemas os bike-sharing e pondo à disposição parques de estacionamento com bicicletas.Neste documento, pretende-se mencionar a criação de um sistema que com a medição de parâmetros físicos, através de sensores fisiológicos e dispositivos Wearable, pode dar a perceber quais as rotas mais otimizadas para um utilizador e de que forma a utilização dos sistema de bike-sharing se pode articular com os restantes transportes públicos da cidade. No final da dissertação, espera-se como resultado uma aplicação Android, denominada SmartBikeEmotion, que pretende permitir aos utilizadores usufruir do que os sistemas de bike-sharing proporcionam, com o acréscimo da adaptação dos circuitos à sua condição física, auxiliando na sua movimentação pela cidade. Esta aplicação será integrada com o projeto BikeEmotion, um projeto de bike-sharing, desenvolvido em consórcio pela Ubiwhere, Ponto.C, Micro I/O, e Universidade de Aveiro.Over the years, the bike-sharing has received a widespread attention due to initiatives to increase the use of bicycles instead of motorized transports. The objective of this change is to minimize the use of urban transport that release greenhouse gases which causes environmental problems and traffic congestion. Take in account the alternative to urban transport and motor vehicles, in order to adopt a green and healthy lifestyle, many cities around the world attempts to provide this lifestyle to its citizens, building bike-sharing systems and making available parking spaces for bicycles.In this paper, we intended to show the creation of a system which with the measurement of physical parameters, through physiological sensors and Wearable devices, may advise about the more optimized routes for the users and how the bike-sharing system can be linked with other public transports. In the end of this thesis, we expect an Android application, called SmartBikeEmotion, which aims to allow the users take advantages of the bike-sharing system, with the strength of the circuits adaption to the user's physical condition, assisting them in the cycling through the city. This application will be integrated with BikeEmotion project, a bike-sharing project, developed in partnership by Ubiwhere, Ponto.C, Micro I / O, and University of Aveiro

Maintaining privacy during continuous motion sensing

Mobile devices contain sensors which allow continuous recording of a user's motion allowing the development of activity, fitness and health applications. With varied applications, the motion sensors present new privacy problems which require protection. This dissertation builds on previous work with activity and fitness machine learning techniques demonstrating the ability to predict medical values from motion data using smartphones. We conduct two clinical trials collecting a data set of eighty-eight patients and forty-five hours of monitoring to analyze the privacy implications of releasing motion data. We extract a comprehensive set of statistical features from all available smartphone sensors and evaluate feature selection techniques and machine learning models. We find we can predict user identity, phone identity, speed, FEV1/FVC, and activity from the motion signal. Designing a privacy protection mechanism for motion data requires a precise understanding of how the signal predicts the sensitive information. We develop algorithms to conduct private feature selection which identifies features useful for prediction. We find that simply blocking all private features significantly reduces the usefulness of the signal for other predictions. We develop a sensitivity estimation framework to calibrate the noise for each private feature requiring an order of magnitude less noise than differential privacy sensitivity. We find adding noise to private features calibrated using the sensitivity estimate is effective at reducing the prediction of five tested target predictions. Our methods hide both user and phone identification while allowing other prediction but cannot hide activity, FEV1/FVC and speed without significantly lowering the accuracy of other predictions. Our methods are still effective when the attacker has prior knowledge of the noise distribution. The methods presented in this dissertation demonstrate the need for privacy in motion data and provide a framework for protecting sensitive user information in motion readings

Accurate Caloric Expenditure of Bicyclists using Cellphones

Biking is one of the most efficient and environmentally friendly ways to control weight and commute. To precisely estimate caloric expenditure, bikers have to install a bike computer or use a smartphone connected to additional sensors such as heart rate monitors worn on their chest, or cadence sensors mounted on their bikes. However, these peripherals are still expensive and inconvenient for daily use. This work poses the following question: is it possible to use just a smartphone to reliably estimate cycling activity? We answer this question positively through a pocket sensing approach that can reliably measure cadence using the phone’s on-board accelerometer with less than 2 % error. Our method estimates caloric expenditure through a model that takes as inputs GPS traces, the USGS elevation service, and the detailed road database from OpenStreetMap. The overall caloric estimation error is 60 % smaller than other smartphone-based approaches. Finally, the smartphone can aggressively duty-cycle its GPS receiver, reducing energy consumption by 57%, without any degradation in the accuracy of caloric expenditure estimates. This is possible because we can recover the bike’s route, even with fewer GPS location samples, using map information from the USGS and OpenStreetMap databases.

Tennessee Blue Book 2021-2022

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