• Visualisations for clinical decision making

    Changes in daily habits can provide important information regarding the overall health status of an individual. This research aimed to determine how meaningful information may be extracted from limited sensor data and transformed to provide clear visualisation for the clinicians who must use and interact with the data and make judgments on the condition of patients. We ascertained that a number of insightful features related to habits and physical condition could be determined from usage and motion sensor data.

    Our approach to the design of the visualisation follows User Centered Design, specifically, defining requirements, designing corresponding visualisations and finally evaluating results. This cycle was iterated three times.

    The User Centered Design method was successfully employed to converge to a design that met the main objective of this study. The resulting visualisations of relevant features that were extracted from the sensor data were considered highly effective and intuitive to the clinicians and were considered suitable for monitoring the behavior patterns of patients.

    We observed important differences in the approach and attitude of the researchers and clinicians. Whereas the researchers would prefer to have as many features and information as possible in each visualisation, the clinicians would prefer clarity and simplicity, often each visualisation having only a single feature, with several visualisations per page. In addition, concepts considered intuitive to the researchers were not always to the clinicians.

    Enumeration of conceived visualisations


    1.1 24 hour usage line graph
    1.2 24 hour usage activity simple
    1.3 24 hour usage activity colors
    1.4 24 hour usage activity bars
    2.1 24 hour motion activity line graph
    2.2 24 hour motion activity window
    2.3 24 hour motion activity window graph
    2.4 24 hour motion events / hour
    2.5 24 hour motion events / hour differences
    3.1 Days usage occupancy times
    3.2 Days usage occupancy hours
    3.3 Days usage occupancy only hours
    3.4 Days usage non-occupancy hours
    3.5 Days usage occupancy hours stacked
    3.6 Days usage occupancy hours stacked areas
    4.1 Days usage # events
    4.2 Days usage # events / day parts
    5.1 Raw usage occupancy
    6.1 Days motion nocturnal inactivity times
    6.2 Days motion nocturnal inactivity hours
    7.1 Days motion # events
    7.2 Days motion # events / day parts
    8.1 Raw motion # events / hour
    8.2 Raw motion activity 15 minute window
    8.3 Raw motion activity 30 minute window



    The double digit identifier is extended by a third digit, which identifies the cycle number in the development process, resulting in the format: ‘(group).(variation).(cycle)’.

    For each main visualisation type, the development of the different versions is shown.

    decision making1


    decision making2a

    decision making2b

    decision making3


    decision making5

    decision making7

    doi: 10.1186/s12911-014-0102-x

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