Demo:meal photo SNS with mutual healthiness evaluation for improving users' eating habits

Abstract We propose a Meal Photo SNS (called HealthyStadium) for improving users’ eating habits by mutually assessing each others’ health. This application’s method is to evaluates pictures of meals leads to the realization of sustainable journaling. In addition, we implement competitive awareness that motivates the users to improve their eating habits by allowing users to share their healthiness ranking amongst the users. Through our study, we confirmed that our system enables users to motivate for improving eating habits. Also we found the number of records is increased by our system

WellComp 2019:second international workshop on computing for well-being

Abstract With the advancements in ubiquitous computing, ubicomp technology has deeply spread into our daily lives, including office work, home and house-keeping, health management, transportation, or even urban living environments. Furthermore, beyond the initial metric of computing, such as “efficiency” and “productivity”, the benefits that people (users) benefit on a well-being perspective based on such ubiquitous technology has been greatly paid attention in the recent years. In our second “WellComp” (Computing for Well-being) workshop, we intensively discuss about the contribution of ubiquitous computing towards users’ well-being that covers physical, mental, and social wellness (and their combinations), from the viewpoints of various different layers of computing. Having strong international organization members in various ubicomp research domains, WellComp 2019 will bring together researchers and practitioners from the academia and industry to explore versatile topics related to wellbeing and ubiquitous computing

Understanding smartphone notifications’ user interactions and content importance

Abstract We present the results of our experiment aimed to comprehensively understand the combination of 1) how smartphone users interact with their notifications, 2) what notification content is considered important, 3) the complex relationship between the interaction choices and content importance, and lastly 4) establish an intelligent method to predict user’s preference to seeing an incoming notification. We use a dataset of notifications received by 40 anonymous users in-the-wild, which consists of 1) qualitative user-labelled information about their preferences on notification’s contents, 2) notification source, and 3) the context in which the notification was received. We assess the effectiveness of personalised prediction models generated using a combination of self-reported content importance and contextual information. We uncover four distinct user types, based on the number of daily notifications and interaction choices. We showcase how usage traits of these groups highlight the requirement for notification filtering approaches, e.g., when specific users habitually neglect to manually filter out unimportant notifications. Our machine learning-based predictor, based on both contextual sensing and notification contents can predict the user’s preference for successfully acknowledging an incoming notification with 91.1% mean accuracy, crucial for time-critical user engagement and interventions

WellComp 2021:fourth international workshop on computing for well-being

Abstract With the advancements in ubiquitous computing, ubicomp technology has deeply spread into our daily lives, including office work, home and house-keeping, health management, transportation, or even urban living environments. Furthermore, beyond the initial metrics commonly applied in computing, such as “efficiency” and “productivity”, the benefits that people (users) benefit on a well-being perspective based on such ubiquitous technology has been greatly emphasized in the recent years. Through the second “WellComp” (Computing for Well-being) workshop, we will intensively discuss about the contribution of ubiquitous computing towards users’ well-being that covers both physical, mental, and social wellness (and their combinations), from the viewpoints of various different layers of computing. Having strong international organization members in various ubicomp research domains, WellComp 2021 will bring together researchers and practitioners from the academia and industry to explore versatile topics related to well-being and ubiquitous computing

Using iOS for inconspicuous data collection:a real-world assessment

Abstract Mobile Crowd Sensing (MCS) is a method for collecting multiple sensor data from distributed mobile devices for understanding social and behavioral phenomena. The method requires collecting the sensor data 24/7, ideally inconspicuously to minimize bias. Although several MCS tools for collecting the sensor data from an off-the-shelf smartphone are proposed and evaluated under controlled conditions as a benchmark, the performance in a practical sensing study condition is scarce, especially on iOS. In this paper, we assess the data collection quality of AWARE iOS, installed on off-the-shelf iOS smartphones with 9 participants for a week. Our analysis shows that more than 97% of sensor data, provided by hardware sensors (i.e., accelerometer, location, and pedometer sensor), is successfully collected in real-world conditions, unless a user explicitly quits our data collection application