CoConUT Framework

… supporting short-term mobile field studies since 2016


The CoConUT Framework was part of my PhD project to assess context and mental ressources during mobile usage in the field. It was centered around the CoConUT app for Android, which acted as a central point for assessing contextual sensor values and heart rate of the proband during field studies. It was complemented by the CoCoQuest app for guiding the proband and asking in-situ questions in the field. The experimental CoCoHat and the Biosensor did not prove to be reliable enough for field usage.

If you are interested in reading my PhD thesis, you can download the PDF here. It was published fully under a Creative Commons license and funded by a netidee grant in 2018.


About the CoCoNut Framework

CoConUT (“Context Collection for non-stationary User Testing”) is a field study toolkit for supporting short-term mobile field studies. It features several apps and wearables which collect quantitative and qualitative data about context and behavior directly in the field.

The CoConUT Framework consists of the following components:

CoConUT App

The CoConUT app for Android collects sensoral data about environmental context and user interaction during short-term mobile field studies directly on the smartphone. As an Android app it collects mobile context as well as frequency of interactions during mobile field studies (for example usability studies) using sensor data on the test device. For evaluation purposes the recorded user trial sessions can be visually explored. This facilitates an assessment of the user’s attention patterns and enables the detection of limited cognitive resources caused by distracting contextual factors. CoConUT Interface

The latest version features the following sensors: noise level, ambient light, touch interaction on the screen, location and Bluetooth devices nearby. CoConUT Visualization

CoCoQuest App

CoCoQuest guides the user through short-term mobile field studies by providing a questionnaire and functionalities to collect in-situ experience through video, sound and photos


The CoConUT Biosensor is an open-source wearable which collects biofeedback of the user by measuring heartbeat, galvanic skin response and skin temperature.

The CoConUT Biosensor is an Open-Source wearable which detects arousal by measuring heart rate, galvanic skin response and body temperature. All of it’s components are Open-Source and can be purchased for around 85 Euro. The collected data is sent via Bluetooth Low Energy to the CoConUT app on the smartphone and is directly integrated.

Unfortunately the Biosensor did not prove to be reliable in field studies.


The CoCoHat is a Raspberry Pi powered hat which collects qualitative video and sound data about environment and user interaction on the device.

The CoCoHat is a complementary headpiece collecting additional video streams of surroundings and user interaction on the test device. When used together during a field trial the data gathered by CoConUT and CoCoHat will give a profound overview over the course of a field study session.

Unfortunately the Biosensor did not prove to be reliable in field studies.


  • Project lead and leading of small teams
  • Scientific concept and realization
  • Setup and scientific coordination of the COSY:lab for user testing, specialization on mobile usability studies
  • Successful acquisition of third-party funds on national level


2016 - 2019


Mental Resources and Context in Mobile Interaction
PhD Thesis (2019)

Errare Mobile Est: Studying the Influence of Mobile Context and Stress on Typing Errors in the Field
Svenja Schröder, Albert Rafetseder, Peter Reichl
11th International Conference on Quality of Multimedia Experience (QoMEX 2019), June 5th - June 7th, 2019, Berlin, Germany (2019)

Exploring the Interplay of Context and Interaction in the Field
Svenja Schröder, Jakob Hirschl, Peter Reichl
10th International Conference on Quality of Multimedia Experience (QoMEX 2018), May 29th - June 1st 2018, Pula, Sardinia, Italy (2018)

CoConUT- Context Collection for Non-Stationary User Testing
Svenja Schröder, Jakob Hirschl, Peter Reichl
Proceedings of 2nd International Workshop on Intelligent Attention Management on Mobile Devices: Workshop in conjunction with MobileHCI 2016. Florence, Italy (September 2016)

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See also