Attention, Comprehension, Execution
Zhida Sun, Nan Cao, Xiaojuan Ma · 2017
The rapid development of biosensors and wearable devices has led to an increasing number of quantified self applications with physiological data. However, conventional graph-style visual representations which have been commonly used for behavior monitoring and control may not be the most applicable biofeedback methods. This is because biosensor data is not intuitive and is hard to manipulate directly and precisely, especially in computer-mediated collaborative interactions. In this work, we explore four different designs, i.e., graphical, illustrative, artistic and ambient representations, by visualizing physiological data in individual settings. Following the Research through Design model, we compare these four designs in terms of their abilities to facilitate biofeedback interpretation through a within-subject controlled experiment with 24 participants. The results suggest that users' visual perception is affected by different design styles.