The present dataset is based on implementing of 3 approaches with respect to the acquisition of driver data. The same one that we propose to use a sensor of concentration of alcohol in the environment (physiological), a sensor that measure the temperature of the defined points on driver’s face (biological) and another one that allows to identify and recognize the thickness of the pupil (visual characteristics).
The present dataset is based on implementing 3 approaches with respect to the acquisition of driver data. The same one that we propose to use a sensor of the concentration of alcohol in the environment (physiological), a sensor that measures the temperature of the defined points on driver’s face (biological) and another one that allows to identify and recognize the thickness of the pupil (visual characteristics).
Number of Instances: 390 (217 for no alcohol presence
173 for alcohol presence with
different concentration)
Number of Attributes: 5 numeric, predictive attributes and the class
Attribute Information:
1. acohol concentration in the car environment in ml/L
2. car environment temperature in degrees Celsius
3. face temperature min in degrees Celsius
4. face temperature max in degrees Celsius
5. pupil ratio
6. class:
1 No acohol presence
2 acohol presence
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Electronic Health Records and clinical longitudinal data have been visualized in a wide range of applications to assist the understanding of the status and evolution of patients. Few studies have objectively assessed these applications. We utilized the insights-based method to objectively assess the effectiveness of an application that visualizes longitudinal data from the Australian national electronic health record. Five professional psychiatrists took part in the assessment study.
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