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How can Smartphone Apps support their users' personality development goals, using Big Data and Machine Learning?
Our vision is to create smartphone apps that know their users well and can therefore support them with their personal development goals or dealing with chronic illness. Are you interested in exploring how apps can support their users by analyzing sensor data and usage logs?
Keywords: social computing, internet of things, big data, data analysis, machine learning
Smartphone apps can get access to data from various sensors as well as logs of user interaction, which allow inferences about users’ behavioral patterns.
For example, the number of sensed Bluetooth devices is an indicator of the time spent around other people. Similarly, frequent use of a calendar app indicates a high effort in planning.
We are interested in making inferences from behavior to the psychological states (e.g. emotions) and characteristics (e.g. personality traits) of the user. For example, from a large amount of time spent around other people we might infer a high level of sociability, and a high effort in planning is indicative of being well-organized.
Smartphone apps can get access to data from various sensors as well as logs of user interaction, which allow inferences about users’ behavioral patterns.
For example, the number of sensed Bluetooth devices is an indicator of the time spent around other people. Similarly, frequent use of a calendar app indicates a high effort in planning.
We are interested in making inferences from behavior to the psychological states (e.g. emotions) and characteristics (e.g. personality traits) of the user. For example, from a large amount of time spent around other people we might infer a high level of sociability, and a high effort in planning is indicative of being well-organized.
Your goal is to investigate how data collected from smartphone sensors and usage logs can be used to predict psychological states and characteristics of their users. These predictions could be very useful for supporting an application's users.
The procedure of this thesis would be as follows:
- briefly review the literature on smartphone sensing
- decide on a set of data sources and associated behavioral variables that you find interesting
- develop and evaluate a predictive model that can predict an interesting self-reported value from the sensor data, using data from an ongoing research study.
Possible self-reported variables of interest are:
- how extraverted, emotionally stable, or self-disciplined someone has been feeling in the last 30 minutes
- how positive, awake, or stressed-out they felt
You should bring:
- Skills and interest in data analysis and machine learning (e.g. using Python or R)
- Interest in psychology and human behavior
Your goal is to investigate how data collected from smartphone sensors and usage logs can be used to predict psychological states and characteristics of their users. These predictions could be very useful for supporting an application's users.
The procedure of this thesis would be as follows:
- briefly review the literature on smartphone sensing - decide on a set of data sources and associated behavioral variables that you find interesting - develop and evaluate a predictive model that can predict an interesting self-reported value from the sensor data, using data from an ongoing research study.
Possible self-reported variables of interest are:
- how extraverted, emotionally stable, or self-disciplined someone has been feeling in the last 30 minutes - how positive, awake, or stressed-out they felt
You should bring:
- Skills and interest in data analysis and machine learning (e.g. using Python or R) - Interest in psychology and human behavior