The purpose of this study is to investigate the effect of the curved display on visual performance and user experience. Eye-tracking technique and self-report were adopted to assess the visual performance when watching multimedia on the curved display. For visual examination, two curvatures were chosen: flat and curved with a radius equal to 4200 mm. A series of 12 clips were presented, consisting of 2 visual tasks and 10 multimedia video clips. The result revealed an improved visual performance in the curved display, especially in terms of visual search and attention. Participants showed an active scan of visual information for a particular target among distractors and quickly responded to the visual cues. The study provides guidance for an effective visual communication through a curved display and can help manufacturers understand the strength and weakness of different display forms.

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Objective: As a cue for desired mood, we attempted to identify types of sitting postures when people are involved in various tasks during their working hours.Background: Physical behaviors in reaction to user contexts were studied, such as automated posture analysis for detecting a subject's emotion. Sitting postures have high feasibility and can be detected robustly with a sensing chair, especially when it comes to an office.Method: First, we attached seven sensors, including six pressure sensors and one distance sensor, to an office chair. In Part 1, we recorded participants' postures while they took part in four different tasks. From the seven sensors, we gathered five sets of data related to the head, the lumbar, the hip, thigh pressure and the distance between the backrest and the body. We classified them into four postures: leaning forward, upright, upright with the lumbar supporting, and leaning backward. In part 2, we requested the subjects to take suitable poses for the each of the four task types. In this way, we compared the matches between postures and tasks in a natural setting to those in a controlled situation. Results:We derived four types of sitting postures that were mapped onto the different tasks. The comparison yielded no statistical significance between Parts 1 and 2. In addition, there was a significant association between the task types and the posture types. Conclusion:The users' sitting postures were related to different types of tasks. This study demonstrates how human emotion can interact with lighting, as mediated through physical behavior. Application:We developed a posture-based lighting system that manipulates the quality of office lighting and is operated by changes in one's posture. Facilitated by this system, color temperatures ranging between 3,000K and 7,000K and illuminations ranging between 300lx and 700lx were modulated.