Intermittent Continuance of Smart Health Devices: A Zone-of-Tolerance Perspective

Smart health and wearable devices have recently received widespread attention from practitioners and scholars. However, intermittent continuance behavior of users is considered to be one of the most important reasons hindering the development of smart health. To address this issue, the current study employs the zone-of-tolerance theory to explore the mechanisms through which intermittent continuance is evoked. In particular, this study develops two new constructs (i.e., performance superiority and performance adequacy), and proposes that they affect intermittent continuance via satisfaction and neutral satisfaction, respectively. Results demonstrated that the effects of the two new variables on intermittent continuance of smart health devices had been fully mediated. This study concludes with theoretical and practical implications

Human Activity Recognition: A Comparison of Machine Learning Approaches

This study aims to investigate the performance of Machine Learning (ML) techniques used in Human Activity Recognition (HAR). Techniques considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Decision Tree with entropy, Random Forest, Gradient Boosting Decision Tree, and NGBoost algorithm. Following the activity recognition chain model for preprocessing, segmentation, feature extraction, and classification of human activities, we evaluate these ML techniques against classification performance metrics such as accuracy, precision, recall, F1 score, support, and run time on multiple HAR datasets. The findings highlight the importance to tailor the selection of ML technique based on the specific HAR requirements and the characteristics of the associated HAR dataset. Overall, this research helps in understanding the merits and shortcomings of ML techniques and guides the applicability of different ML techniques to various HAR datasets

An overview of data fusion techniques for internet of things enabled physical activity recognition and measure

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Measure (PARM) has been widely recognised as a key paradigm for a variety of smart healthcare applications. Traditional methods for PARM relies on designing and utilising Data fusion or machine learning techniques in processing ambient and wearable sensing data for classifying types of physical activity and removing their uncertainties. Yet they mostly focus on controlled environments with the aim of increasing types of identifiable activity subjects, improved recognition accuracy and measure robustness. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to an open and dynamic uncontrolled ecosystem by connecting heterogeneous cost-effective wearable devices and mobile apps and various groups of users. Little is currently known about whether traditional Data fusion techniques can tackle new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand potential use and opportunities of Data fusion techniques in IoT enabled PARM applications, this paper will give a systematic review, critically examining PARM studies from a perspective of a novel 3D dynamic IoT based physical activity collection and validation model. It summarized traditional state-of-the-art data fusion techniques from three plane domains in the 3D dynamic IoT model: devices, persons and timeline. The paper goes on to identify some new research trends and challenges of data fusion techniques in the IoT enabled PARM studies, and discusses some key enabling techniques for tackling them

Making Sense of Long-Term Physical Activity Tracker Data: The challenge of Incompleteness

Millions of people have already collected weeks, months and even years of data about their own health and physical activity levels. The potential is enormous for use in personal applications as well as for public health analysis of large populations at low cost. However, the reality is many people fail to wear their tracker and record data all day every day especially over the long-term. The resulting incompleteness in data poses an important challenge for interpreting long-term tracker data, in terms of both making sense of it and in dealing with the uncertainty of inferences based on it. Surprisingly, there has been little work into defining the problem, its extent and how it should be measured and addressed. This thesis tackles this key challenge and we demonstrate the need for a term to describe and quantify this challenge. We introduce the term, adherence, which quantifies the completeness in such data. We also offer interface designs that accounted for adherence to support self-monitoring and reflection. Bringing these together, we provide broader definitions and guidelines for incorporating adherence when making sense of long-term physical activity tracker data, both in personal applications and in public health research results. This thesis is based on three studies. First is a semester-long study of tracker use by 237 University students. Second is a study of 21 existing long-term physical activity trackers and provided the first richly qualitative exploration of physical activity and adherence of such users. It also evaluated the iStuckWithIt, a long-term physical activity data user interface, and reported on insights gained within and as aided by a tutorial and reflection scaffolding. In the final study, we drew on 12 diverse datasets, for 753 users, with over 77,000 days with data and 73,000 days missing to explore the impact of different definitions of adherence and methods for dealing with its implications