Published papers

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.

Nowadays, various kinds of sensors are available due to expansion of the smartphones. Along with this, the research that records the action on a daily basis has become very popular. Distinction of transport is also a part of the studies. The major result from resent studies has been detected those transport by using the sensors such as GPS or a microphone or an acceleration sensor. However, since this is one of the biggest cause to encourage the consumption of the batteries, it is not practical to detect only with a smartphone. I suggested in this paper the system to detect transport adopting acceleration sensor and evaluated the detection accuracy by applying various kinds of method of machine learning. Moreover, I evaluated power saving of this system.

Nowadays, various kinds of sensors are available due to expansion of the smartphones. Along with this, the research that records the action on a daily basis has become very popular. Distinction of transport is also a part of the studies. The major result from resent studies has been detected those transport by using the sensors such as GPS or a microphone or an acceleration sensor. However, since this is one of the biggest cause to encourage the consumption of the batteries, it is not practical to detect only with a smartphone. I suggested in this paper the system to detect transport adopting acceleration sensor and evaluated the detection accuracy by applying various kinds of method of machine learning. Moreover, I evaluated power saving of this system.

Nowadays, various kinds of sensors are available due to expansion of the smartphones. Along with this, the research that records the action on a daily basis has become very popular. Distinction of transport is also a part of the studies. The major result from resent studies has been detected those transport by using the sensors such as GPS or a microphone or an acceleration sensor. However, since this is one of the biggest cause to encourage the consumption of the batteries, it is not practical to detect only with a smartphone. I suggested in this paper the system to detect transport adopting acceleration sensor and evaluated the detection accuracy by applying various kinds of method of machine learning. Moreover, I evaluated power saving of this system.

In,this research, we propose a software model for the dynamic distribution of software components across a wide-area network, and we provide a software environment to realize the proposed model. In the dynamic distributed software model, software modules can autonomously migrate among computers. This feature allowes dynamic distributed software to have the advantage of dynalnic load balancing, fault tolerance, scalability, optimization of computing environment, and the processing support for small-size portable devices. Moreover, we designed and implemented a dynamic distributed software toolkit named "Meta Space".