Context Information

RS810 in comparison to electrocardiogram (ECG) devices. The results showed, that short term Heart Rate Variability (HRV) measurement is as reliable and valid as stationary de-vices. Several commercial applications exist, that support the Polar H6.

Figure 5.3.: Polar H6 [Ele13]

The H6 supports the Bluetooth smart standard, which allows the wireless transmission with low energy consumption leading to longer battery lifetimes. The transmission range is approximately 10 meters, which should be sufficient for most scenarios.

5.3. Context Information

Figure 5.4.: Context information used for correction

Measures for correction of physiological data (shown in figure 5.4) fall into the class of controlling influencing factors of these physiological measures. For example, temperature and movement sensor integrated in the Q-sensor can be used to control interpretation of EDA values, which rise because of arm movement or changing temperature. For the heart rate, movement and position are important to control the interpretation. As introduced in chapter 2.3, heart rate can be influenced by position or movement of the body. In this case, the integrated step sensor of the smartphone is used to determine movement. The posi-tion, if an user is sitting or standing, might be determined with a combination of different sensors. The movement sensor integrated in the Q-Sensor collects information about arm movement. In combination with the step sensor sitting, standing and walking might be distinguished reliably to a certain level.

Figure 5.5.: Context information used for improvement of the interpretation of physiolog-ical signals

As mentioned, context information is also used for direct improvement of interpretation in the model. Context information, which falls into this category is shown in figure 5.5.

Information, like e.g. location or movement in general, can be used to offer additional in-formation to determine user state more accurate. For example, different user interfaces or information in the application can be offered based on location-awareness or difficulty of a game can be lowered based on performance. Context information of this category is not

only received by integrated hardware sensors, but also by applications, like performance measures or a current score.

Some information can be used in both categories. For example movement falls into the category of correction because it can be used for correction of HR and EDA, when a person is moving. Movement falls also in the category of improvement because of the fact that movement can also be used for general improvement of current user state, e.g. adaptation of user interface, when a person is walking.

5.3.2. Location

Location can be determined in different ways, for example by GPS sensors, cell ID or Wi-Fi. The three mentioned ways for location determination have different advantages and disadvantages. GPS is very power consuming, depending on the refresh rate. Other dif-ferences between the sensors can be found in accuracy and time needed for refreshment of position. The accuracy of position estimation can depend on the different conditions.

Bad weather or walls of a building might influence the accuracy of GPS. Within buildings, position accuracy of location acquisition over Wi-Fi might be better than GPS, depending on the situation in the building.

With help of GPS sensor, it can also be determined, if a person is travelling fast, e.g. by car or train. The technical details of implementation and integration into the model will be introduced in detail chapter 6 and 7.

5.3.3. Movement

An aspect regarding controlling the effects of the environment on the user is movement. By using built-in sensors like gyroscope or magnetometer, movement can be determined with help of different detection algorithms. Different operating systems, like e.g. Android, of-fer a specialized step application programming interface (API), delivering the steps taken without the need to implement detection algorithms.

If an user is moving, the distraction from using an application is bigger than when an ap-plication is used in a non-moving scenario. When moving, users have to split attention to pay attention to the environment to e.g. prevent accidents. The interpretation of affective and cognitive state can be enhanced, e.g. difficulty can be reduced, when walking.

When using physiological data, movement information is also needed to control influ-encing effects of movement on the data. For example, heart rate may rise when walking fast.

5.3. Context Information

5.3.4. Context Information - Application Information

Depending on the application, several pieces of information might be useful to be inte-grated into the model. Performance statistics can be used in games or learning applica-tions. In other applications performance measures like the time needed for a certain task may be an indication for current state. The value is not useful for control, but for improve-ment of user state interpretation.

Values for statistics have to be transmitted from the application to the interpretation model. This measure depends strongly on the type of application. Some applications may also not offer statistics or it might not be reasonable. The usage of statistics are further described in the section of the specific applications.

5.3.5. Other Context Information

Besides the previous described context information, modern smartphones offer sensors to measure even more context information. Examples for other sensors integrated in smart-phones are proximity sensor, microphone, photometer or magnetometer. The proximity sensor allows to determine if a user is holding the phone near the face, the microphone measures the noise level and the photometer can measure the illuminance of the environ-ment.

Based on these sensors, extensions of the model like identifying with help of the pho-tometer if a user is sleeping (in a dark room) or not could be added. These information is not integrated in the model, as the model concentrated on movement, location and appli-cation information. But the possibility exists to integrate them.

5.3.6. Conclusion

As shown in this section, different context information can be collected. They can be used for control of measured physiological signals, as well as for improvement of the user state interpretation itself.

From the presented context information, location, movement and application informa-tion like performance are important for this work. Movement is primary used for cor-rection of physiological signals, performance and location for improvement of user state interpretation.

For location, GPS sensor was selected for this work. As the power consumption might be higher than with the other sensors, a refresh of position information is only done occa-sionally. For determination of movement, the integrated step sensor API is used, having the advantage of not needing a separate implementation of step determination based on accelerometer etc. Out of the application information, performance was chosen to be a

suitable measure for most scenarios. It might especially be a benefit for applications in-volving any kind of performance as e.g. games. The implementation and technical details will be described in chapter 7.