Page History

The CoConUT project (http://coconut.cosy.wien) features smartphone apps which collect sensor data (location, speed, noise, nearby Bluetooth devices, heart rate, etc.) for each participant during mobile field studies. Result is a time series which shows information about the context and possibly interesting events the field study participants encountered („Why did the participant slow down on the corner?“, „Why were so many people present nearby during this time period?“, etc.). The data sets hereby consist of sensor data collected during a field study on the participants' smartphones. These time series data should be visualized in a web the smartphone app itself and enriched by meaningful analyses to enable exploration and potentially reasoning. Focus hereby lies on the visualization for small device screens (smartphone / tablet).

Requirements: You have already attended either the Vis and/or the HCI lecture and had good grades. You are fit in Android programming. You don't shy away from statistics.

The CoConUT project (http://coconut.cosy.wien) features smartphone apps which collect sensor data (location, speed, noise, nearby Bluetooth devices, heart rate, etc.) for each participant during mobile field studies. Result is a time series which shows information about the context and possibly interesting events the field study participants encountered („Why did the participant slow down on the corner?“, „Why were so many people present nearby during this time period?“, etc.). The data sets hereby consist of sensor data collected during a field study on the participants' smartphones. These time series data should be visualized in the smartphone app itself and enriched by meaningful analyses to enable exploration and potentially reasoning. Focus hereby lies on the visualization for small device screens (smartphone / tablet).

Requirements: You have already attended either the Vis and/or the HCI lecture and had good grades. You are fit in Android programming. You don't shy away from statistics.

While being on the move the error rate for smartphone usage presumably is higher. But which kinds of errors occur and when do they occur? Do they occur at home, at one's workplace, while being in transit? Is there a difference whether the user is stressed or relaxed? This thesis will explore the aforementioned questions for a specific usage scenario. For example by means of an enhanced Open Source keyboard for Android a deeper look at typing errors on the go could be made. Several other scenarios are possible: mobile security (e.g. while entering the locking pattern), navigation, and so on.

Requirements: You are fit in Android programming. You don't shy away from statistics.

There are several frameworks which try to automatically assess the context and activity of the user (e.g. the Google Activity framework), but automatically detecting current context and activity remain a challenge. One possibility is to ask the user via notifications and obtain contextual data on a subjective basis, which is highly obtrusive, but quite correct. Another way is using Machine Learning or heuristics to determine the circumstances by automatically evaluating data on the smartphone itself.

In this work, you will explore options to assess information about the user's context and activities.

Requirements: You are fit in Android programming. You don't shy away from statistics. This preferably is either a P1/P2 or master thesis topic, but it's also possible to take this as an highly ambitious bachelor thesis topic.

Requirements: You have already attended the HCI lecture and had good grades. You are fit in Android programming. You don't shy away from statistics.

Requirements: You have already attended the HCI lecture and had good grades. You are fit in Android programming. You don't shy away from statistics.

This is an umbrella topic for several works potentially possible in the scope of the CoConUT framework. If you're interested in working on the project, or have some great ideas yourself, please contact Svenja Schröder (Svenja.schroeder@univie.ac.at).

Diese Bachelorarbeit ist eine Zusammenarbeit mit einem Studierenden der klinischen Psychologie. Gegenstand ist die Entwicklung und Evaluation einer Smartphone-Applikation, die mithilfe einer Kombination verschiedener psychologisch erprobter Methoden die mentale Gesundheit bei Jugendlichen und jungen Erwachsenen verbessern soll. Während der klinisch-psychologische Teil der Bachelorarbeit sich mit den klinisch-psychologischen Konzepten und der Durchführung der Evaluation auseinandersetzt, wird der informatische Teil die Entwicklung eines motivationalen Konzeptes (Serious Gaming) und die Umsetzung der App mittels eines User Centered Design Cycles umfassen. In einem iterativen soll die Usability der App schrittweise verbessert werden, sodass am Ende des gemeinsamen Aufwandes ein funktionierender App-Prototyp steht.

Voraussetzungen: HCI-Vorlesung muss besucht worden sein, Kenntnisse in Programmierung für Android erforderlich