Abstract Zico Pratama Putra 29 Nov 2017
Investigation of Trend Identification on Mobile Audio Graph
While researchers have performed numerous studies to understand human interpretation to visual graphs in reading, comprehending and interpreting displayed data; visually impaired (VI) users still face many challenges that prevent them from fully benefiting from this graph. Consequently, this affects their understanding of data and in turn reduce their role in the collaborative task with their sighted peers in both educational and working environments. It is understood how people perceive and interpret visual graphs, but that in comparison there is very little understanding of how people perceive and interpret auditory graphs. Our goal in creating the Mobile Auditory Graph, MAG, is to understand how well VI user analyses the trend with a means to understand data chart in the mobile device in which the audio presented.
The research proposed here relates to the interaction between VI users to develop a further under-investigated area of Multi-modal Auditory Graph (MMAG). The purpose is to analyze the ability of VI users to identify the trend of the audiograph chart using additional modalities. The testing methods are performed using the Android device installed an audiograph application that has several sets of graphs. The results of the playback test mode -that sequencing the note sounds- is compared against the swipe test mode with haptic feedback.