Shape changing robot is an approach towards locomotion on uncertain terrain due to its omni-directional features. However, the current locomotion method for such robots rely on discontinuous rolling. We propose a free form locomotion: an omni directional continuous crawling for deformable robots. This method introduce continuous shifting of contact surface similar to amoeba movement. A Mochibot that has thirty two telescopic legs is developed to verify the proposed locomotion method. Through the experiments, we have confirmed that the robot can track smooth paths: straight, smooth, and hand written curves. We also evaluate errors between desired and measured trajectories of the robot.

In today's ubiquitous computing environment where users carry, manipulate, and interact with an increasing number of networked devices, applications and web services, human attention is the new bottleneck in computing. It is therefore important to minimize a user's mental effort due to notifications, especially in situations where users are mobile and using multiple wearable and mobile devices. To this end, we propose Attelia II, a novel middleware that identifies breakpoints in users' lives while using those devices, and delivers notifications at these moments. Attelia II works in real-time and uses only the mobile and wearable devices that users naturally use and wear, without any modifications to applications, and without any dedicated psycho-physiological sensors. Our in-the-wild evaluation in users' multi-device environment (smart phones and smart watches) with 41 participants for 1 month validated the effectiveness of Attelia. Our new physical activity-based breakpoint detection, in addition to the UI Event-based breakpoint detection, resulted in a 71.8% greater reduction of users' perception of workload, compared with our previous system that used UI events only. Adding this functionality to a smart watch reduced workload perception by 19.4% compared to random timing of notification deliveries. Our multi-device breakpoint detection across smart phones and watches resulted in about 3 times greater reduction in workload perception than our previous system.

In today's ubiquitous computing environment where the number of devices, applications and web services are ever increasing, human attention is the new bottleneck in computing. To minimize user cognitive load, we propose Attelia, a novel middleware that identifies breakpoints in user interaction and delivers notifications at these moments. Attelia works in realtime and uses only the mobile devices that users naturally use and wear, without any modifications to applications, and without any dedicated psycho-physiological sensors. Our evaluation proved the effectiveness of Attelia. A controlled user study showed that notifications at detected breakpoint timing resulted in 46% lower cognitive load compared to randomly-timed notifications. Furthermore, our “in-the-wild” user study with 30 participants for 16 days further validated Attelia's value, with a 33% decrease in cognitive load compared to randomly-timed notifications.

We propose a novel technique that aggregates multiple sensor streams generated by totally different types of sensors into a visually enhanced video stream. This paper shows major features of SENSeTREAM and demonstrates enhancement of user experience in an online live music event. Since SENSeTREAM is a video stream with sensor values encoded in a two-dimensional graphical code, it can transmit multiple sensor data streams while maintaining their synchronization. A SENSeTREAM can be transmitted via existing live streaming services, and can be saved into existing video archive services. We have implemented a prototype SENSeTREAM generator and deployed it to an online live music event. Through the pilot study, we confirmed that SENSeTREAM works with popular streaming services, and provide a new media experience for live performances. We also indicate future direction for establishing visual stream aggregation and its applications.