Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Internalizing Data Collection: Personal Analytics as an Investigation of the Self

Personal analytics, aka self-tracking, is the practice of using a digital device to track aspects of behavior, such as exercise habits, heart rate, sleep patterns, location, diet, and a host of other data points. This dissertation is an exploration of “self” in self-tracking, informed by theories of subjectivity, autonomy, power and knowledge. As a technological intervention, self-tracking devices change how we experience our own body and behavior. They also serve as methods to digitize human behavior. This data is combined with other data and processed using computational methods. Self-tracking devices are both personal and bureaucratic. They are devices used for self-care and institutional processes. As mediating objects, they occupy a multifaceted position that they share with other forms of mediated experience. Like social media, which is both a form of personal expression and a way to track users’ behavior, self-tracking participates in changing attitudes about surveillance. People are willing to subject themselves to surveillance and are largely unaware or unconcerned with the ways in which self-surveillance is the same thing as institutional surveillance. This study positions self-tracking as a practice of institutional population management, not simply personalized exercise tools. A Fitbit might seem to simply measure a “step,” an identifiable metric that exists regardless of whether it is counted. Yet, how can this metric be considered neutral and objective when its institutional purpose guides its development? Thinking of measurement as neutral ignores the process by which anything comes to be measured. All kinds of decisions—about what to count, how to count it, and what to do with the data—are made prior to the end user’s experience. Measurement is a cultural activity and thus the outcome of this data collection is never neutral with respect to power. By looking at fitness-tracker privacy policies, workplace wellness programs, data sharing practices, and advertising materials, I trace the discursive practices surrounding self-tracking. As we surveil our bodies and behavior, we enact a focused attention upon the self. Understanding the consequence of this focus is crucial to understanding how data operates in today’s economy. My overall critique of data in this dissertation concerns how the focus on self obscures the institutional uses and abuses of data. The epistemic affordances of data flow in multiple directions. Self-tracking devices offer the promise to reveal hidden data about the self. They accomplish something different—they create the means to recraft the self into something else entirely. They make the self into an entity that is knowable and therefore able to be the subject of market transactions and manipulated by institutions

The Internet of Things Will Thrive by 2025

This report is the latest research report in a sustained effort throughout 2014 by the Pew Research Center Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-LeeThis current report is an analysis of opinions about the likely expansion of the Internet of Things (sometimes called the Cloud of Things), a catchall phrase for the array of devices, appliances, vehicles, wearable material, and sensor-laden parts of the environment that connect to each other and feed data back and forth. It covers the over 1,600 responses that were offered specifically about our question about where the Internet of Things would stand by the year 2025. The report is the next in a series of eight Pew Research and Elon University analyses to be issued this year in which experts will share their expectations about the future of such things as privacy, cybersecurity, and net neutrality. It includes some of the best and most provocative of the predictions survey respondents made when specifically asked to share their views about the evolution of embedded and wearable computing and the Internet of Things

Smart Sensing Technologies for Personalised Coaching

People living in both developed and developing countries face serious health challenges related to sedentary lifestyles. It is therefore essential to find new ways to improve health so that people can live longer and can age well. With an ever-growing number of smart sensing systems developed and deployed across the globe, experts are primed to help coach people toward healthier behaviors. The increasing accountability associated with app- and device-based behavior tracking not only provides timely and personalized information and support but also gives us an incentive to set goals and to do more. This book presents some of the recent efforts made towards automatic and autonomous identification and coaching of troublesome behaviors to procure lasting, beneficial behavioral changes

Computational Commensality: from theories to computational models for social food preparation and consumption in HCI

Food and eating are inherently social activities taking place, for example, around the dining table at home, in restaurants, or in public spaces. Enjoying eating with others, often referred to as “commensality,” positively affects mealtime in terms of, among other factors, food intake, food choice, and food satisfaction. In this paper we discuss the concept of “Computational Commensality,” that is, technology which computationally addresses various social aspects of food and eating. In the past few years, Human-Computer Interaction started to address how interactive technologies can improve mealtimes. However, the main focus has been made so far on improving the individual's experience, rather than considering the inherently social nature of food consumption. In this survey, we first present research from the field of social psychology on the social relevance of Food- and Eating-related Activities (F&EA). Then, we review existing computational models and technologies that can contribute, in the near future, to achieving Computational Commensality. We also discuss the related research challenges and indicate future applications of such new technology that can potentially improve F&EA from the commensality perspective

Recognition of cooking activities through air quality sensor data for supporting food journaling

Abstract Unhealthy behaviors regarding nutrition are a global risk for health. Therefore, the healthiness of an individual's nutrition should be monitored in the medium and long term. A powerful tool for monitoring nutrition is a food diary; i.e., a daily list of food taken by the individual, together with portion information. Unfortunately, frail people such as the elderly have a hard time filling food diaries on a continuous basis due to forgetfulness or physical issues. Existing solutions based on mobile apps also require user's effort and are rarely used in the long term, especially by elderly people. For these reasons, in this paper we propose a novel architecture to automatically recognize the preparation of food at home in a privacy-preserving and unobtrusive way, by means of air quality data acquired from a commercial sensor. In particular, we devised statistical features to represent the trend of several air parameters, and a deep neural network for recognizing cooking activities based on those data. We collected a large corpus of annotated sensor data gathered over a period of 8 months from different individuals in different homes, and performed extensive experiments. Moreover, we developed an initial prototype of an interactive system for acquiring food information from the user when a cooking activity is detected by the neural network. To the best of our knowledge, this is the first work that adopts air quality sensor data for cooking activity recognition