Thesis (Ph. D.)--University of Wisconsin--Madison, 1972. / Typescript. Vita. Includes bibliographical references.

Smart Homes (SHs), as subsets of the Internet of Things (IoT), make use of Machine Learning and Arti cial Intelligence tools to provide technology-enabled solutions which assist their occupants and users with their Activities of Daily Living (ADL). Some SH provide always-present, health management support and care services. Having these services provided at home enables SH occupants such as the elderly and disabled to continue to live in their own homes and localities thus aiding Ageing In Place goals and eliminating the need for them to be relocated in order to be able to continue receiving the same support and services. Introducing and interconnecting smart, autonomous systems in homes to enable these service provisions and Assistance Technologies (AT) requires that certain interfaces in, and connections to, SH are exposed to the Internet, among other public-facing networks. This introduces the potential for cyber-physical attacks to be perpetrated through, from and against SH. Apart from the actual threats posed by these attacks to SH occupants and their homes, the potential that these attacks might occur can adversely a ect the adoption or uptake of SH solutions. This thesis identi es key attributes of the di erent elements (things or nodes and rooms or zones) in SHs and the relationships that exist between these elements. These relationships can be used to build SH security baselines for SHs such that any deviations from this baseline is described as anomalous. The thesis demonstrates the application of these relationships to Anomaly Detection (AD) through the analysis of several hypothetical scenarios and the decisions reached about whether they are normal or anomalous. This thesis also proposes an Internet of Things Digital Forensics Framework (IDFF), a Forensics Edge Management System (FEMS), a FEMS Decision-Making Algorithm (FDMA) and an IoT Incident Response plan. These tools can be combined to provide proactive (autonomous and human-led) Digital Forensics services within cyber-physical environments like the Smart Home.

This is a qualitative, interpretive research study of how six senior citizens’ aged 70-85 describe their use and need of Smart home technology for their independent living and welfare. Data was collected through technology probing and interviews. Data analysis was done using a thematic method. The research was underpinned by the theoretical model Unified Theory of Acceptance and Use of Technology (UTAUT2). The research setting was senior citizens’ living in Care housing rented out by the municipality owned company. The findings show that technology is perceived by the senior citizens’ to be something must be taught and takes effort. Senior housing need to be better designed for its senior tenants. It was also found that if technology is used to ensure safety, fall detection for instance, trust and sufficient support is crucial. The findings suggest it could be difficult to assess your own need and to realize when assistance technology is needed. It also found that the use of technology was perceived as being individual and as something that cannot be treated collectively. It was suggested that health professionals do have some influence on the senior citizen and their use of technology. The findings of this study was compared to previous studies and how it relates to the determinants and moderating factors suggested by the theoretical model UTAUT2.

AIMS: This study investigated the perception of generic and disease-specific Health-Related Quality of Life (HRQoL) for adults living with hearing impairment (HI) in Aotearoa/New Zealand (NZ). This study aimed to answer three questions: (1) What is the perception of HRQoL amongst adults with hearing impairment in NZ? (2) How do these perceptions compare to adults with HI living in other countries for which we have data? (3) What are the demographic and audiometric variables related to device ownership? METHOD: HRQoL, demographic, and audiometric information was collected from 126 adults in NZ. The following demographic information was collected: age, relationship length, hours worked per week, income, ancestry, sex, level of education, city size, and sexual orientation. The following audiologic information was also collected: ownership of hearing aids (HA), ownership of hearing assistance technology (HAT), better-ear pure-tone average (BEPTA), worse-ear pure-tone average (WEPTA), and signal-to-noise ratio loss (SNR loss). HRQoL information was collected using the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36; Ware & Sherbourne, 1992), and the Hearing Handicap Inventory (HHI) for both elderly (HHIE) and adults (HHIA; Ventry & Weinstein, 1982; Newman, Weinstein, Jacobson, & Hug, 1991). Variables discriminating HA and HAT owners from non-owners were also analysed. RESULTS: The relationship between demographic variables and HRQoL scores revealed that only age and income were significant. Audiometric variables had significant relationships with disease-specific HRQoL scores, as well as HA and HAT ownership. Finally, disease-specific HRQoL scores and all audiometric variables differentiated HA owners from non- owners, but demographic variables did not. Generic HRQoL scores and all audiometric variables differentiated HAT owners from non-owners. CONCLUSIONS: These results suggest that the negative impacts of HI on HRQoL as reported overseas are also present in NZ, and that not only do audiometric variables including SNR loss are related to HRQoL, but HRQoL is a significant predictor for HA and HAT ownership. Further QoL research is warranted amongst the HI population in NZ to identify and understand any causal relationships present amongst these variables. Furthermore, HRQoL instruments and a test of speech understanding in noise have been shown to provide additional meaningful information, and therefore clinicians might consider including them during consultation.