Human Activity Recognition (HAR) Using Wearable Sensors and Machine Learning

Humans engage in a wide range of simple and complex activities. Human Activity Recognition (HAR) is typically a classification problem in computer vision and pattern recognition, to recognize various human activities. Recent technological advancements, the miniaturization of electronic devices, and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments, alongside smart wearable sensors, have opened the door to numerous opportunities for adding value and personalized services to citizens. Vision-based and sensory-based HAR find diverse applications in healthcare, surveillance, sports, event analysis, Human-Computer Interaction (HCI), rehabilitation engineering, occupational science, among others, resulting in significantly improved human safety and quality of life. Despite being an active research area for decades, HAR still faces challenges in terms of gesture complexity, computational cost on small devices, and energy consumption, as well as data annotation limitations. In this research, we investigate methods to sufficiently characterize and recognize complex human activities, with the aim to improving recognition accuracy, reducing computational cost and energy consumption, and creating a research-grade sensor data repository to advance research and collaboration. This research examines the feasibility of detecting natural human gestures in common daily activities. Specifically, we utilize smartwatch accelerometer sensor data and structured local context attributes and apply AI algorithms to determine the complex gesture activities of medication-taking, smoking, and eating. This dissertation is centered around modeling human activity and the application of machine learning techniques to implement automated detection of specific activities using accelerometer data from smartwatches. Our work stands out as the first in modeling human activity based on wearable sensors with a linguistic representation of grammar and syntax to derive clear semantics of complex activities whose alphabet comprises atomic activities. We apply machine learning to learn and predict complex human activities. We demonstrate the use of one of our unified models to recognize two activities using smartwatch: medication-taking and smoking. Another major part of this dissertation addresses the problem of HAR activity misalignment through edge-based computing at data origination points, leading to improved rapid data annotation, albeit with assumptions of subject fidelity in demarcating gesture start and end sections. Lastly, the dissertation describes a theoretical framework for the implementation of a library of shareable human activities. The results of this work can be applied in the implementation of a rich portal of usable human activity models, easily installable in handheld mobile devices such as phones or smart wearables to assist human agents in discerning daily living activities. This is akin to a social media of human gestures or capability models. The goal of such a framework is to domesticate the power of HAR into the hands of everyday users, as well as democratize the service to the public by enabling persons of special skills to share their skills or abilities through downloadable usable trained models

An Investigation of the Usability and Desirability of Health and Fitness-Tracking Devices

This study investigated the usability and functionality of 6 different fitness tracking wristbands that have been suggested to improve and encourage healthy behaviors. While many previous studies assess the accuracy and behavioral effects of fitness tracking devices, limited research has been done to analyze the usability and desirability of these products. Participants were asked to rate their impressions of six fitness tracking devices - the Garmin Vivofit, Jawbone Up24, Fitbit One, Basis B1 Band, Misfit Shine, and the Tom Tom Multisport – before and after usage. Participants were also asked to describe the main factors contributing to their overall preference and likelihood to purchase and/or use each device. Results indicate that participants are initially more likely to favor, small, lightweight devices that have a display. After wearing the devices, the most valued features were attractiveness, long battery life, waterproof, and a heart rate monitor. The study suggest that a “one size fits all approach” to the design of fitness tracking devices may not be the most effective method to promote the actual use of the technology

Cognitive Processing of Consumer Credit Offers

This thesis contributes to the understanding of consumer credit decisions from a psychological point of view by investigating the influence of cognitive processes on perceived credit attractiveness and credit choice. A total of seven empirical lab studies conducted with university students in 2014 and 2015 can be grouped into three connected streams: The first stream focuses on the question whether the level of mental abstraction influences preferences for specific credit aspects (e.g., a low annual percentage rate). Participants’ choice among several credit alternatives with full information on all credit aspects was not affected by construal level. Nor did construal level influence what participants perceived to be important when making up their mind about credit offers in general. The second stream focuses on the question how mental abstraction and the 0 in 0%-interest credits interact to influence credit evaluation as well as the choice of the product that is to be financed. Contrary to assumptions based on previous findings, participants did not react particularly positive towards 0%-interest credit offers in general, nor did mental abstraction influence their reaction. Also, the other way round, additional findings suggest that the prominent display of the number 0 in 0%-interest credit advertisement does not induce a higher level of mental abstraction and consequently does not influence the type of product to be financed on credit. The third stream focuses on the question whether consumers are more willing to take up a 0%-interest credit when they process information in a more intuitive and heuristic way. The idea behind this is that the central feature of a 0%-interest rate may divert attention from less advantageous aspects of such credits (e.g., an expensive mandatory residual debt insurance). The findings from the studies reject this hypothesis. Furthermore, it was investigated whether intended hedonic product use, as opposed to intended utilitarian product use, leads to higher credit attractiveness under intuitive information processing because of a carry-over effect of positive affect from anticipated use situations. No indication of such an interaction was found in the respective study. The thesis offers detailed interpretation of the results with a focus on possible explanations for the absence of significant effects. Despite the absence of significant results, the thesis makes a valuable contribution to the respective area of research. It highlights the need for research on 0%-interest credit offers as a new form of credit that is likely to accelerate the trend of growing debt accumulation. Also, the thesis is the first to apply two broadly successful theories – construal level theory and System 1 / 2 information processing – to aspects of decision-making in a consumer credit context. The results even more so show how little is known about how consumers process information when making such important decisions. The synthesis of previous findings reported in the theory section of this thesis furthermore points out the alarmingly low levels of credit understanding in the general population. This highlights the need for governments, and consumer protection institutions to invest more effort into the development and application of measures targeted to improve credit understanding and money management in a broader sense