Graphically Visualizing Quantitative Smart Home Data
Zulas, Amanda Leah
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Elder care in the developing world is increasingly becoming an issue of too many patients for too few workers. Smart homes suggest a way to help keep older adults in their homes for longer while allowing caregivers to remotely observe their progress. In order for the smart home to be a useful tool for caregivers, such as nurses, physicians, and family, health metrics and activity data must be available and easy to interpret. Unfortunately, visualizations to date have been created by researchers for researchers and not for caregivers. The goal of the current study was to determine the types of data visualization that will facilitate the detection of changes in longitudinal sleep data by caregivers. Experiment 1 manipulated proximity (high, low) and graph type (bar, line). Experiment 2 manipulated trend lines (with, without). Results from Experiment 1 showed a significant increase in overall performance for low proximity bar graphs. Results from Experiment 2 showed a significant increase in overall performance for trend lines. These findings suggest low proximity bar graphs may increase overall performance of users for longitudinal changes over time. Trend lines may also increase performance, although only small gains were found in the current study.