Published papers

© 2019 Elsevier Ltd 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.

© Copyright held by the owner/author(s). 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 first “WellComp” (Computing for Well-being) workshop, we 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 2018 will bring together researchers and practitioners from the academia and industry to explore versatile topics related to wellbeing and ubiquitous computing.

© 2016 ACM. Users of digital devices are increasingly confronted with a tremendous amount of notifications that appear on multiple devices and screens in their environment. Today many users own different ubiquitous devices such as a smartphone, a tablet, a notebook and a smartwatch. If an email client is installed on every device an incoming email produces up to four notifications - one on each device. In the future, we will receive notifications from an increasing number of ubiquitous devices. Therefore, we need smart attention management for incoming notifications as well as novel ways to present and interact with notifications. One way for a less interrupting attention management could be the use of ambient representations of incoming notifications. This workshop brings together researchers and practitioners from academia and industry to explore how the flood of notifications on different computing devices and in smart environments can be managed, to avoid information overload.

This paper presents a new architecture for integrating Networked Appliances, called Virtual Network Appliance (VNA). Instead of merely connecting appliances seen in existing approaches, VNA architecture enables users to create a networked appliance by assembling functional components of existing appliances. Users can achieve dynamic adaptation and personal customization, exploiting VNA.

Currently, everyone can detect and store many life-log data very easily with the spread of smart phones or wearable devices and large scale data storage. Wide variety and large scale life-log data are very useful to self behavior change, medical care, education, and social analytics. Existing researches of human behavior change using life-log data focus on individual human behavior change. But nowadays everyone have communication equipment like smart phones. Group behavior change is very useful to keep workers' health in company and/or managing amount of practice in a sport team. Group has many kind of human relations, for example, manager and normal member. Therefore techniques for individual human behavior change is not effective to groups. The biggest aim of our research is to build the model of group behavior change. Specially, we focus on team behavior change. In this paper, we propose six types of group behavior model based on the "competition" and "collaboration" technique. We used encouraging team modification application in sports team and laboratory for keeping the sit-up exercise for 3 weeks. Sit-up exercise is very important to preventing the injury and keep fit for the sport team and the laboratory member. We create 8 groups in a team. We evaluate and consider this model based on the change of the self-efficacy and number of sit-up activities.

Currently, everyone can detect and store many life-log data very easily with the spread of smart phones or wearable devices and large scale data storage. Wide variety and large scale life-log data are very useful to self behavior change, medical care, education, and social analytics. Existing researches of human behavior change using life-log data focus on individual human behavior change. But nowadays everyone have communication equipment like smart phones. Group behavior change is very useful to keep workers' health in company and/or managing amount of practice in a sport team. Group has many kind of human relations, for example, manager and normal member. Therefore techniques for individual human behavior change is not effective to groups. The biggest aim of our research is to build the model of group behavior change. Specially, we focus on team behavior change. In this paper, we propose six types of group behavior model based on the "competition" and "collaboration" technique. We used encouraging team modification application in sports team and laboratory for keeping the sit-up exercise for 3 weeks. Sit-up exercise is very important to preventing the injury and keep fit for the sport team and the laboratory member. We create 8 groups in a team. We evaluate and consider this model based on the change of the self-efficacy and number of sit-up activities.

Recent technological trends on mobile/wearable devices and sensors have been enabling increasing number of people to collect and store their "lifelog" easily in their daily lives. Beyond exercise behavior change of individual user, our research focus is on the behavior change of teams, based on life-logging technologies and information sharing. In this paper, we propose and evaluate six different types of information sharing model among team members for their exercise promotion, leveraging concepts of "competition" and "collaboration". According to our experimental mobile web application for exercise promotion and extensive user study among 64 total users for three weeks, the model with "competition" technique resulted the most effective performance for competitive teams such as sport teams.

Currently, everyone can detect and store many life-log data very easily with the spread of smart phones or wearable devices and large scale data storage. Wide variety and large scale life-log data are very useful to self behavior change, medical care, education, and social analytics. Existing researches of human behavior change using life-log data focus on individual human behavior change. But nowadays everyone have communication equipment like smart phones. Group behavior change is very useful to keep workers' health in company and/or managing amount of practice in a sport team. Group has many kind of human relations, for example, manager and normal member. Therefore techniques for individual human behavior change is not effective to groups. The biggest aim of our research is to build the model of group behavior change. Specially, we focus on team behavior change. In this paper, we propose six types of group behavior model based on the "competition" and "collaboration" technique. We used encouraging team modification application in sports team and laboratory for keeping the sit-up exercise for 3 weeks. Sit-up exercise is very important to preventing the injury and keep fit for the sport team and the laboratory member. We create 8 groups in a team. We evaluate and consider this model based on the change of the self-efficacy and number of sit-up activities.