Application for pre-processing and visualization of electrodermal activity wearable data

Using sensors to gather physiological data about users can provide valuable insights that are not available merely using traditional measures. Electrodermal activity (EDA) can act as an indicator for both physiological and psychological arousal. Measuring arousal has several application areas. For instance, prolonged and often recurring high arousal levels can indicate that a person is suffering from chronic stress. At the other extreme, for example, in elderly care constant low arousal levels can signal that the senior citizens are not getting enough activity and attention from the care personnel. In the context of events, measurement of arousal can indicate when the persons get excited and when they are more calm. This study presents a pilot study of EDA measurements conducted during a trade fair. Providing timely and meaningful information for a group of people being measured, however, requires pre-processing the data and creating visualizations that enable both individual and collective level sense-making of the results. The aim of this study was to develop a process and an open source application that can automatically pre-process large amounts of data from wearable sources, and create visualizations, to be used in events for immediate sense-making.