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.
Educative visualisation illustrating separation processes
The full HD videos available are here:
Facilitating insight into a simulation model using visualization and dynamic model previews
Model simplification, by replacing iterative steps with unitary predictive equations, can enable dynamic interaction with a complex simulation process. Model previews extend the techniques of dynamic querying and query previews into the context of ad hoc simulation model exploration. A case study is presented within the domain of counter-current chromatography. The relatively novel method of insight evaluation was applied, given the exploratory nature of the task. The evaluation data show that the trade-off in accuracy is far outweighed by benefits of dynamic interaction. The number of insights gained using the enhanced interactive version of the computer model was more than six times higher than the number of insights gained using the basic version of the model. There was also a trend for dynamic interaction to facilitate insights of greater domain importance.
Completely graphical interface
ProMISE 2 introduces a new completely graphical user interface. A visual representation of the column (top of image) allows selection of visual elements which enables specific input parameters. When input parameters are changed the visual column representation adapts to these parameters, and predictive results are shown real time in graph and numerical forms (bottom of image).
Freely available as Open Source software under the GNU GPL-3.0 License
Probabilistic Model for Immiscible Separations and Extractions (ProMISE)
Chromatography models, liquid-liquid models and specifically Counter-Current Chromatography (CCC) models are usually either iterative, or provide a final solution for peak elution. This paper describes providing a better model by finding a more elemental solution. A completely new model has been developed based on simulating probabilistic units. This model has been labelled ProMISE: Probabilistic Model for Immiscible phase Separations and Extractions, and has been realised in the form of a computer application, interactively visualising the behaviour of the units in the CCC process. It does not use compartments or cells like in the Craig based models, nor is it based on diffusion theory. With this new model, all the CCC flow modes can be accurately predicted. The main advantage over the previously developed model, is that it does not require a somewhat arbitrary number of steps or theoretical plates, and instead uses an efficiency factor. Furthermore, since this model is not based on compartments or cells like the Craig model, and is therefore not limited to a compartment or cell nature, it allows for an even greater flexibility.
Universal Counter Current Chromatography modelling based on Counter Current Distribution
There is clearly a need for a model which is versatile enough to take into account the numerous operating modes and pump out procedures that can be used with counter-current chromatography (CCC). This paper will describe a universal model for counter-current chromatography based on counter-current distribution. The model is validated with real separations from the literature and against established CCC partition theory. This universal model is proven to give good results for isocratic flow modes, as well as for cocurrent CCC and dual flow CCC, and will likely also give good results for other modes such as intermittent CCC.