With the increasing elderly population and longer life expectancies, smart wearable technologies are playing an important role in facilitating caregivers to monitor elderly people remotely. Aifloo’s wristband is one smart wristband which can collect various data, predict activities and detect abnormalities to enable elderly people to live independently at home. However, too much information and poor visualizations will cause huge difficulties for caregivers to interpret the data. Six caregivers were interviewed in this study to investigate what data is relevant to monitor elderly people and how they interpret the different designed displays. The main results show that alarms, fall incidents and medication compliance are the most important. Besides, caregivers place a greater emphasis on holistic views of data and they want to highlight abnormal behaviors and alerts. In the end, design guidelines for the information system to present data meaningfully and intuitively are generated. / Med ett ökande antal äldre och en ökande medellivslängd kommer smart, bärbar teknologi att spela en större roll i äldrevården för att övervaka de äldre. Aifloos armband är en smart teknologi som kan samla in olika former av data, förutsäga aktiviteter och upptäcka avvikande och onormala beteenden, vilket kan användas av äldre som bor självständiga i sena egna hem. Stora mängder data, och dåliga visualiseringar av dem, orsakar svårigheter för vårdgivare att tolka datan. I den här studien har sex vårdgivare intervjuats för att utforska vilken data som är relevant för dem, och hur de kan tolka information ifrån en grupp olika gränssnitt. Studiens resultat visar att alarm, fallolyckor och översikt över hur de äldre efterföljer sina medicinska recept är viktigast. Vårdgivarna lägger en större vikt vid att förstå datan holistiskt, och de vill synliggöra avvikande beteendemönster och varningar. Slutgiltligen presenteras riktlinjer för hur IT-system kan designas för att presentera data på ett meningsfullt och intuitivt vis.

Design and analysis of computer experiments is an area focusing on efficient data collection (e.g., space-filling designs), surrogate modeling (e.g., Gaussian process models), and uncertainty quantification. Survival analysis focuses on modeling the period of time until a certain event happens. Data collection, prediction, and uncertainty quantification are also fundamental in survival models. In this dissertation, the proposed methods are motivated by a wide range of real world applications, including high-performance computing (HPC) variability data, jet engine reliability data, Titan GPU lifetime data, and pine tree survival data. This dissertation is to explore interfaces on computer experiments and survival analysis with the above applications. Chapter 1 provides a general introduction to computer experiments and survival analysis. Chapter 2 focuses on the HPC variability management application. We investigate the applicability of space-filling designs and statistical surrogates in the HPC variability management setting, in terms of design efficiency, prediction accuracy, and scalability. A comprehensive comparison of the design strategies and predictive methods is conducted to study the combinations' performance in prediction accuracy. Chapter 3 focuses on the reliability prediction application. With the availability of multi-channel sensor data, a single degradation index is needed to be compatible with most existing models. We propose a flexible framework with multi-sensory data to model the nonlinear relationship between sensors and the degradation process. We also involve the automatic variable selection to exclude sensors that have no effect on the underlying degradation process. Chapter 4 investigates inference approaches for spatial survival analysis under the Bayesian framework. The Markov chain Monte Carlo (MCMC) approaches and variational inferences performance are studied for two survival models, the cumulative exposure model and the proportional hazard (PH) model. The Titan GPU data and pine tree survival data are used to illustrate the capability of variational inference on spatial survival models. Chapter 5 provides some general conclusions. / Doctor of Philosophy / This dissertation focus on three projects related to computer experiments and survival analysis. Design and analysis of the computer experiment is an area focusing on efficient data collection, building predictive models, and uncertainty quantification. Survival analysis focuses on modeling the period of time until a certain event happens. Data collection, prediction, and uncertainty quantification are also fundamental in survival models. Thus, this dissertation aims to explore interfaces between computer experiments and survival analysis with real world applications. High performance computing systems aggregate a large number of computers to achieve high computing speed. The first project investigates the applicability of space-filling designs and statistical predictive models in the HPC variability management setting, in terms of design efficiency, prediction accuracy, and scalability. A comprehensive comparison of the design strategies and predictive methods is conducted to study the combinations' performance in prediction accuracy. The second project focuses on building a degradation index that describes the product's underlying degradation process. With the availability of multi-channel sensor data, a single degradation index is needed to be compatible with most existing models. We propose a flexible framework with multi-sensory data to model the nonlinear relationship between sensors and the degradation process. We also involve the automatic variable selection to exclude sensors that have no effect on the underlying degradation process. The spatial survival data are often observed when the survival data are collected over a spatial region. The third project studies inference approaches for spatial survival analysis under the Bayesian framework. The commonly used inference method, Markov chain Monte Carlo (MCMC) approach and the approximate inference approach, variational inference's performance are studied for two survival models. The Titan GPU data and pine tree survival data are used to illustrate the capability of variational inference on spatial survival models.

The concept of using the vibration transmitted through the structure of space systems whilst they are in flight for monitoring purposes is proposed and analysed.The performed patent review seems to indicate that this technique is not currently used despite being, in principle, a good way to obtain valuable knowledge about the spacecraft’s condition. Potential sources of vibration were listed and some of them were down-selected via a trade-off analysis for implementation in a numerical model of a CubeSat structure. Models were proposed for the sources chosen and implemented in the Ansys Workbench software, along with a simplified structure designed to be representative of a generic picosatellite mission.The results confirmed very different amplitude and frequency ranges for the sources of interest, which would make it difficult to monitor them with one type of sensor.Basic system requirements for accelerometer operating under space conditions were derived and commercial sources were identified as already having the technologies needed.The conclusion was a positive evaluation of the overall concept, although revising negatively the initial expectations for its performance due to the diversity encountered in the sources.

Back pain and injury are a global health issue and are a leading cause of work and activity absence. Prevention would not only save those affected from the burden of pain and discomfort, but would also save people from loss of over 290 million workdays annually and save the healthcare system billions of dollars in expenses per year. Successful research and development of a wearable technology capable of comprehensively monitoring spinal postures that are leading causes of back pain and injury can result in prevention of mild to severe back pain and injury for high-risk people. To accomplish this, the Smart Spine Tape is being developed with specific focus on accuracy, usability, and accessibility, all of which are important factors to consider when engineering for a wide array of populations. Accuracy was assessed using three human participants, with spinal angle data of the Smart Spine Tape being compared to established motion analysis technology data. Prototypes of the device showed promise in the ability to accurately measure spinal postures, but inconsistencies between samples and trials indicated that further development is necessary. Usability and accessibility were assessed using ten human participants who completed one workout each and reported on the tape’s comfort, durability, and ease of use, as well as their thoughts on how much they would be willing to pay for a fully functional version of the device. Participants reported high comfort, high durability, and moderate ease of use throughout their experiences, with the average price range that they would be willing to pay being between $25 and $75. Future directions have been identified that address inconsistencies in data collected by the Smart Spine Tape, possibly caused by inconsistent resistive properties of the piezoresistive ink and plastic deformation of the tape during testing. These future directions involve modifying testing, material, and fabrication methods.

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