Research

Senbay

A Novel Smartphone-based Platform for Capturing and Integrating Synchronously-recorded Video/Sensor Data Streams

 The platform embeds collected sensor data into a video frame using an animated two-dimensional barcode using real-time video processing. The video-embedded sensor data, Senbay Video, can be easily restreamed to other people and applications. Our performance evaluations and case studies show that the Senbay makes it much easier to collect and share time-synchronous sensor and video data.

Happy Board

A Platform for Mutual Watch-Over among the Elderly Using PAN and Gamification

HappyBoard is a platform for realizing a mutual watch over the elderly in the local community and demonstrated its effectiveness through about 10 months of experiments. In the platform, mutual watching was realized by acquiring health information from Personal Area Network, shooting and sharing smile images using smartphones, and gaming by mission, score, ranking.

Sapplication

A Lifelog Sharing Platform for Promoting Team-level Behavior Change

Sapplication Platform is a platform for enhancing and measuring team-level behavior change using information sharing among team members in the ubiquitous environment for the first time. As an intervention method for a team, Sapplication Platform can share lifelog data via six types of information sharing models that are based on the “competition” and “collaboration” techniques on existing researches.

Aaron

Promoting Team-level Behavior Change by Lifelog Sharing​

Beyond exercise behavior change of individual users, our research focus is on the behavior change of teams, based on lifelogging technologies and lifelog sharing. In this paper, we propose and evaluate six different types of lifelog sharing models among team members for their exercise promotion, leveraging the concepts of “competition” and “collaboration.”

Ripken

Preventing Baseball Players’ Throwing-related Injury using a Wearable Motion Sensor on an Elbow

Baseball players are susceptible to shoulder and elbow injuries due to having an inappropriate throwing form and overload. In this research, we developed an algorithm for detecting inappropriate throwing form using a wearable motion sensor on an elbow. The algorithm can be applied not only for recording a count of throws but also real-time feedback for preventing injury in practices and games.