This theses is divided into three theses to examine different aspects of physiological data and context information as an input for mobile applications. The integration of physiolog-ical signals allows to analyze the state of a user, which can be used to adapt an application to the users’ needs. As described in the State of the art section, many integrations of phys-iological data into stationary applications have been done, but a lack of models for usage scenarios beyond stationary settings exist. One big challenge is the conceptualization and implementation of a model for mobile scenarios. This requires to examine if and how ex-isting models for mainly stationary usage can be used and integrated in mobile scenarios.
In mobile scenarios many disturbing factors appear, which can at least be better con-trolled or do not occur in stationary scenarios. For example movement can influence phys-iological signals like heart rate. Furthermore, mobile applications have several limiting factors. They are currently limited e.g. in screen size and battery. Measures have to be taken, to guarantee the robustness and reliability of the model and interpretation of user state in different scenarios.
As a result of the requirements of the mobile scenario, a better knowledge of the current environment and situation of the user is needed. This requires to gather context informa-tion to integrate into the model for a better interpretainforma-tion of user state.
In the following the theses of this work, investigating the mentioned problems and ques-tions, will be introduced and in detail described in the following subchapters. Approaches for the solutions will be introduced within the following chapters.
4.1. Mobile Scenarios
Before defining the theses in detail, this section will give a brief overview of how a mobile scenario is defined within this work and how they differ from laboratory settings. Statistics showed, that mobile devices are used in many different situations. Situations of usage can differ by locations, movement or distraction of the environment. When a person is not at home or office, applications have to deal with typical limitations like battery lifetime and network coverage. Movement on the other hand can split the attention of an user, as the environment needs a certain amount of attention to prevent accidents. Many of these defined situations can be determined by context information.
4.2. Theses
Thesis I:
The combination of physiological signals and context information improves the interpretation of user state.
Mobile devices offer a broad range of sensors, which collect additional information about the context. We assume that context information helps to improve the interpretation of physiological signals and the model for interpretation itself by controlling influencing factors on physiological signals and providing additional information about the environ-ment. This raises the following question:
• Can context information provided by application and mobile phone improve the interpretation quality of user state?
Chapter 4.3 gives a short overview how this thesis will be investigated in this work.
Thesis II:
A general model for the combination of physiological signals and context information as an input for user state interpretation in mobile applications can be defined.
The current state of the art in this area was described in the previous chapter. Only [SKC+12] investigated the usage of physiological data in mobile scenarios briefly. We assume that current models can be extended and modified to suit the needs of mobile scenarios. This raises the following questions:
• Can a model be defined that supports different types of applications, e.g. learning and entertainment?
• Can a model be defined, that supports the usage in mobile scenarios, e.g. usage of an application during travel without loss of quality in interpretation and without impairing the user?
A short overview, how the thesis and these two questions will be investigated is given in chapter 4.4.
Thesis III:
The model developed in thesis II is robust enough to handle the loss of input channels.
4.3. Combination of Physiological Signals and Context Information
In mobile scenarios, there is always the possibility that one of the input channels gets lost because of e.g. battery life time or connection problems. We assume that the model is robust and reliable enough to compensate the temporary or permanent loss of one input channel and deliver reliable results. This thesis raises the following question:
• Can loss of a channel, e.g. by empty battery, be compensated without a big drop in interpretation quality?
Handling of channel loss and reliability in this work, will shortly be introduced in chap-ter 4.5.
4.3. Combination of Physiological Signals and Context Information
A difference between stationary and mobile scenarios is the broad range of possible envi-ronments and situations, which can occur and influence the behavior of an user. Modern mobile devices offer sensors like step-sensor, Global Positioning System (GPS) and many more. These sensor offer valuable information of the context the user is interacting in.
An analysis of available sensor data will be done and available information categorized.
The data gets preprocessed and is then transmitted to the different input channels to cor-rect external influencing factors on physiological signals. Furthermore, context informa-tion itself is used to improve the interpretainforma-tion of user state, because it can be used as an additional information itself instead of a solely usage to control influencing aspects on physiological signals.
Thesis I aims to integrate context information into the model defined in thesis II, allow-ing a more reliable interpretation of physiological signals.
4.4. Model for Mobile Applications
Existing approaches for the integration of physiological data into applications need to be examined on their suitability for mobile scenarios. For mobile scenarios, processing of data needs to be fast and continuously, as the situation might change within seconds.
In this thesis existing approaches for user state interpretation are analyzed, used and extended for a broad range of mobile applications. The characteristics of mobile scenarios and its influence on usage of physiological data are investigated. As an input, physiolog-ical signals need to be chosen based on the restrictions given by the mobile scenario. Sen-sors for measurement need to be as small and unobtrusive as possible. The signals need to be continuously processed to achieve a high reliability in the interpretation of user state.
The output of the model is the current state of the user, like e.g. affective or cognitive state, which allows mobile applications to adapt the interface, content or other components.
With thesis II, a model for integration of physiological data into mobile applications will be validated. This allows the adaptation of user interfaces or content in applications based on the current state of the user to achieve the goal of a higher user experience in comparison to applications without support of physiological data.
4.5. Reliability of Input Channel Handling
The mobile scenario requires a certain robustness of the model, to handle the loss or cor-ruptness of input signals. The model needs to adjust if one of the physiological signals or parts of the context information is missing. With the validation of thesis III, the proposed model is adapted to handle the loss of input channels with an minimal loss in accuracy of user state interpretation.
To assure a robust model, the question, how big the accuracy of the model is, when one measurement or input channel falls away, has to is investigated to give an appropriate es-timation for different scenarios. This information itself can be used as feedback within the model. The model needs to be tested with several configurations for different applications to give an estimation.
4.6. Conclusion
As introduced in thesis II, a model for integration of physiological signals into mobile applications is needed. Different physiological signals will be examined about their suit-ability for mobile usage. Existing models will be analyzed and adapted to fit the usage scenarios. As a result the model will deliver an estimation of current user state, which is defined by different categories. These values can be used as an input in mobile applica-tions of different areas for adaptation.
The combination of physiological signals and context information gathered by inte-grated sensors of mobile devices leads to more accurate and reliable results. User status is refined and influences on the physiological signals controlled. Furthermore, the combina-tion of both input channels leads to a higher robustness and reliability of the model.