Development of a Sensor Framework for the Determination of Vital Signs by the Example of a Fitness Application

Today we live in a digitalized, highly technical world which gives birth to new technical innovations on a daily basis. This gives us the opportunity to use these innovations to gain access to a vast field of information. If used in a responsible way this can be a large advantage and enrichment compared to past times. It, however, only gives us access to information that has already been recorded and stored by someone else. Due to a human having recorded this information it is not always dispassionate. This type of information is difficult to analyze. For example asking a random person how he feels will normally result in "Fine, thanks." But this is only a personal opinion. The information we gained is subject to the person’s conception. It cannot be analyzed correctly or compared with the same type of information collected from another person. We need to find a way to collect information from people that can be evaluated and compared. Thanks to a few technical innovations we have the possibility to do so. Simple medical instruments are capable of collecting human vital parameters (e.g., heart rate, blood pressure, oxygen saturation, etc.), giving us the possibility to look inside a person by watching how his body reacts to a certain situation. Going back to the example above the person could just as well be lying. Watching his heartbeat increase while answering the question would lead us to the suspicion that this person is lying. These vital parameters are measurable and within a range of variances depending on the person and can give us valuable information about them. In other words we have sensors that can sense these parameters and offer this measured data. In a next step we want to analyze this data in an efficient way. Nowadays, almost everybody carries a smartphone in their pocket, most equipped with a Bluetooth radio. The data from our sensors will therefore be sent to a smartphone, saved and evaluated on it.
This work describes a Bluetooth framework used to connect Bluetooth sensors to a smartphone running Android 4.1 (API Level 16). To validate the results and test the sensors, I implemented a fitness application called XFitXtreme. The collected sensor data is held and graphically evaluated at the end of the workout. This project is documented here.