Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research

Community Time-Activity Trajectory Modelling based on Markov Chain Simulation and Dirichlet Regression

Accurate modeling of human time-activity trajectory is essential to support community resilience and emergency response strategies such as daily energy planning and urban seismic vulnerability assessment. However, existing modeling of time-activity trajectory is only driven by socio-demographic information with identical activity trajectories shared among the same group of people and neglects the influence of the environment. To further improve human time-activity trajectory modeling, this paper constructs community time-activity trajectory and analyzes how social-demographic and built environment influence people s activity trajectory based on Markov Chains and Dirichlet Regression. We use the New York area as a case study and gather data from American Time Use Survey, Policy Map, and the New York City Energy & Water Performance Map to evaluate the proposed method. To validate the regression model, Box s M Test and T-test are performed with 80% data training the model and the left 20% as the test sample. The modeling results align well with the actual human behavior trajectories, demonstrating the effectiveness of the proposed method. It also shows that both social-demographic and built environment factors will significantly impact a community's time-activity trajectory. Specifically, 1) Diversity and median age both have a significant influence on the proportion of time people assign to education activity. 2) Transportation condition affects people s activity trajectory in the way that longer commute time decreases the proportion of biological activity (eg. sleeping and eating) and increases people s working time. 3) Residential density affects almost all activities with a significant p-value for all biological needs, household management, working, education, and personal preference.Comment: to be published in Computers, Environment and Urban Syste

Location Based Indoor and Outdoor Lightweight Activity Recognition System

In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care, where inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user’s daily activities, requiring a minimal infrastructure

Tappigraphy: continuous ambulatory assessment and analysis of in-situ map app use behaviour

While map apps on smartphones are abundant, their everyday usage is still an open empirical research question. With tappigraphy – the quantification of smartphone touchscreen interactions – we aimed to capture continuous data stream of behavioural human-map app usage patterns. The current study introduces a first tappigraphy analysis of the distribution of touchscreen interactions on map apps in 211 remotely observed smartphone users, accumulating a total of 42 days of tap data. We detail the requirements, setup, and data collection to understand how much, when, for how long, and how people use mobile map apps in their daily lives. Supporting prior research, we find that on average map apps are only sparsely used, compared to other apps. The longitudinal fluctuations in map use are not random and are partly governed by general daily and weekly human behaviour cycles. Smartphone session duration including map app use can be clearly distinguished from sessions without any map apps used, indicating a distinct temporal behavioural footprint surrounding map use. With the transfer of the tappigraphy approach to a mobile map app use context, we see a promising avenue to provide research communities interested in the underlying behavioural mechanisms of map use a continuous, in-situ momentary assessment method

Experiences and perceptions of the role of palliative and end of life care in heart failure: A modified grounded theory study.

Heart failure (HF) is a progressive, life-limiting illness affecting around 750,000 people in the UK, with a mortality rate of 50% within four years. A large body of qualitative research demonstrates variable quality of HF care at the end of life, difficulties in identifying the dying phase, poor communication, and a failure to achieve a ‘good’ death. However little is known about the assessment of the need for palliative care, the recognition of the last days of life, or the extent to which these considerations are communicated to patients and carers and to the wider multi-disciplinary team. Greater understanding of the transition points from HF diagnosis to death may inform future service planning, including the most appropriate model of palliative care to apply to this patient group. Thus, the aim of this study was to explore experiences of giving or receiving a prognosis and managing the transition point from diagnosis to palliative and end of life care for those with HF. The study involved two stages. The first was a systematic review of the uptake of the Liverpool Care Pathway (LCP) in order to assess current utilisation of end of life care pathways. The second utilised a modified constructivist grounded theory methodology to assess experiences of giving and receiving a prognosis, combining semi-structured interviews with clinicians, observations of clinic and home visit appointments, followed by a series of longitudinal semi-structured interviews with thirteen patients with HF and nine carers. The systematic review demonstrated that the LCP was utilised for less than half of all dying patients. Interviews with clinicians revealed frustration and uncertainty about the contested nature of HF diagnosis and prognosis. Most clinicians rejected the concept of HF as a terminal illness in their everyday practice, and expressed uncertainty about roles and responsibilities for end of life care, alongside a reluctance to actively plan for end of life for individual patients. In contrast, some clinicians demonstrated the ability to deliver problem-based, individualised care but sometimes felt constrained by the perceived lack of multi-disciplinary advanced care planning. Most patients and carers talked about death and dying in general terms but felt that HF specific end of life considerations did not apply to them. They placed much more importance on understanding the emergence of their symptoms and negotiating everyday restrictions. Most patients had not made any decisions about advance care directives, and reported no prognostic discussions with clinicians. Overall, the majority of participants rejected notions of HF as a terminal illness in favour of day to day management and maintenance, despite obvious deteriorations in disease stage and needs over time. This is the first known study exploring the experiences of prognostic communication at all stages of the HF disease trajectory. Findings raise questions regarding the pragmatic utility of the concept of HF as a terminal illness and have implications for future HF care pathway development. A key recommendation emerging from this study is that notions of prognosis should be ultimately rejected for HF care, and be replaced with a problem-based approach to care which combines elements of active and palliative care from diagnosis onwards, alongside regular assessments of communication preferences

Privacy-preserving human mobility and activity modelling

The exponential proliferation of digital trends and worldwide responses to the COVID-19 pandemic thrust the world into digitalization and interconnectedness, pushing increasingly new technologies/devices/applications into the market. More and more intimate data of users are collected for positive analysis purposes of improving living well-being but shared with/without the user's consent, emphasizing the importance of making human mobility and activity models inclusive, private, and fair. In this thesis, I develop and implement advanced methods/algorithms to model human mobility and activity in terms of temporal-context dynamics, multi-occupancy impacts, privacy protection, and fair analysis. The following research questions have been thoroughly investigated: i) whether the temporal information integrated into the deep learning networks can improve the prediction accuracy in both predicting the next activity and its timing; ii) how is the trade-off between cost and performance when optimizing the sensor network for multiple-occupancy smart homes; iii) whether the malicious purposes such as user re-identification in human mobility modelling could be mitigated by adversarial learning; iv) whether the fairness implications of mobility models and whether privacy-preserving techniques perform equally for different groups of users. To answer these research questions, I develop different architectures to model human activity and mobility. I first clarify the temporal-context dynamics in human activity modelling and achieve better prediction accuracy by appropriately using the temporal information. I then design a framework MoSen to simulate the interaction dynamics among residents and intelligent environments and generate an effective sensor network strategy. To relieve users' privacy concerns, I design Mo-PAE and show that the privacy of mobility traces attains decent protection at the marginal utility cost. Last but not least, I investigate the relations between fairness and privacy and conclude that while the privacy-aware model guarantees group fairness, it violates the individual fairness criteria.Open Acces

The Diagnosis and Management of Heart Failure across Primary and Secondary Care

This thesis is centred on the complex arena of heart failure diagnosis and management across the primary-secondary care interface, including service delivery models and the utility of natriuretic peptides in triage of patients. The thesis combines qualitative and quantitative methodologies to identify barriers to heart failure care and to test strategies for overcoming these. The findings were:1. GPs found heart failure difficult to diagnose and treat due to clinical uncertainty, lack of awareness of the relevant research evidence and organisational issues including lack of access to diagnostics.2. With regard to specialists variable opinions and practice in diagnosis and management of heart failure in hospitals and across primary- secondary care were confirmed, these centred on diagnostic difficulties, treatment issues and service delivery problems.3. A GP-specialist led one-stop diagnostic clinic facilitated expedient, accurate diagnosis of left ventricular systolic dysfunction.4. An integrated heart failure service across primary and secondary care delivered evidence based therapy, patient and carer education and access to social and palliative care for patients with heart failure.5. Natriuretic peptide measurement had high negative predictive value for excluding heart failure in a consecutive GP referred cohort.6. Electrocardiography was not as accurate at excluding heart failure as suggested by national guidelines. 7. Use of N-terminal pro B-type natriuretic peptide as a pre-screening test for secondary care referral may have reduced potential referrals, but the low specificity of the test and high prevalence of confounding factors in the screened population increased demand on diagnostic services and did not lead to cost savings. Conclusions The diagnostic and treatment difficulties identified by GPs and hospital specialists are dependent on a complex interplay of patient, clinician and organisational factors. Barriers need to be overcome in locality specific and multi-faceted implementation strategies across primary-secondary care. This thesis described an integrated heart failure diagnosis and management system that overcame these barriers and delivered accurate diagnosis and modern evidence based treatment. The relatively poor positive predictive value and low specificity of natriuretic peptides in real life practice meant that large numbers of patients with raised BNP/NT proBNP did not have heart failure due to left ventricular systolic dysfunction. This thesis demonstrated that the prognostic power of BNP/NT proBNP extended beyond LVSD to most cardiac conditions. Ideally, all patients with raised natriuretic peptides deserve a full cardiac assessment including echocardiography, followed by optimal use of evidenced based pharmacotherapy and health professional support. We need to find ways of providing expedient diagnostic and treatment services to these patients especially in rationed health care systems such as the NHS. Until this issue is addressed widespread natriuretic peptide use is unlikely within the UK