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Hosub Lee


Publications by Hosub Lee (bibliography)

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Lee, Hosub, Choi, Young Sang and Lee, Sunjae (2012): Mobile posture monitoring system to prevent physical health risk of smartphone users. In: Proceedings of the 2012 International Conference on Uniquitous Computing 2012. pp. 592-593.

With the widespread use of a smartphone, users tend to use their smartphone for a long period of time in unhealthy postures; bending forward the neck and watching the relatively small screen closely with concentration. If users keep such unhealthy postures for a long time, they are susceptible to musculoskeletal disorders and eye problems such as cervical disc and myopia, respectively. To prevent users from having these diseases, we propose a new methodology to monitor the posture of smartphone users with built-in sensors. The proposed mechanism estimates various values representing user postures like the tilt angle of the neck, viewing distance, and gaze condition of the user, by analyzing sensor data from a front-faced camera, 3-axis accelerometer, orientation sensor, or any combination thereof, and warns the user if estimated values are maintained within the abnormal range over the allowed time. Via the proposed mechanism, users are able to be aware of their unhealthy postures, and then try to correct their postures.

© All rights reserved Lee et al. and/or ACM Press

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Lee, Hosub and Choi, Young Sang (2011): Fit your hand: personalized user interface considering physical attributes of mobile device users. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 59-60. http://dx.doi.org/10.1145/2046396.2046422

We present a mobile user interface which dynamically reformulates the layout based on the touch input pattern of users. By analyzing the touch input, it infers users' physical characteristics such as handedness, finger length, or usage habits, thereby calculates the optimal touch area for the user. The user interface is gradually adapted to each user by automatically rearranging graphic objects such as application icons to the most easy-to-touch positions. To compute the optimal touch area, we designed software architecture and implemented an Android application which analyzes touch input and determines the touch frequency in specific screen areas, the handedness and hand size of users. As proof of concept, this research prototype shows acceptable performance and accuracy. To decide which items should be placed in the optimal touch area, we plan to integrate our machine learning algorithm which prioritizes applications according to the context of users into the proposed system.

© All rights reserved Lee and Choi and/or ACM Press

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