Visualization of residents in long-term care centres through mobile natural user interfaces (NUI)

Large volumes of formal data and informal information are generated in daily workflow activities of caregivers at long-term care centers. Health data are captured formally through record keeping using paper based forms for regular updates; however, capturing informal information related to resident activities is more challenging. This unstructured data covers social contacts, family events, therapy sessions, and other happenings. The challenges arise firstly from digitizing and aggregating these data sets, because in long-term care, both datasets are essential to assess and support well-being. Secondly, visual analytics seeks to provide caregivers with much better and more effective ways to understand changes in residents' status over long durations, while improving their services immediately. Automated processing and comparison of data is valuable yet human judgment is required to apply analyses to the care of specific residents and develop support across similar groups. This suggests that the integration of automated analysis methods and interactive visualization methods is necessary. Thirdly, direct, multi-sensor handheld devices promise a set of natural input modality in providing interaction techniques such as speech, gesture, touch, and other sensor-based techniques that may facilitate just-in-time ease of analysis. In our research, we concentrate on providing effective visual analytics tools combined with appropriate natural user interfaces (NUI). In this poster, we present a set of NUI designs towards creating a social media platform for caregivers, which integrates automated analysis methods and natural interaction techniques to enable caregivers to capture, store, visualize, and analyze both formal data and informal information. Our research will evaluate whether NUI's make a difference in supporting long-term caregivers.