## 3 Results  
### Data Visualization Using Activity Data Plotter  
The first part of the data analysis was performed using the data visualization comparison component of the system. For each activity, 600 datasets were annotated and used as input for the plots. To add stability and increase the reliability of the data, the mean value was calculated for every two sets and used as input, mitigating any random errors of the sensors. The goal was to observe the differences between each activity as represented by the different input sensors. The resulting plots were divided into four sections, each section corresponding to an activity: 1 st section--fast walk; 2 nd section--slow walk; 3 rd section--resting; and 4 th section--climbing up stairs.  
Figure 2 illustrates the x, y, and z accelerations for each activity type. As can be observed, in the case of resting, the readings are stable and low in amplitude because the force of gravity is measured and the subject is not moving. The slow walk readings are characterized by moderate and regular oscillations, while the fast walk readings have larger oscillations, reflecting the increased speed. It can clearly be observed that it is easy to distinguish between these three activities. In the first part of plot, the x and y accelerations for climbing up stairs do not respect the pattern present in the rest of plot. This is because in the first session the smartwatch was worn inside-out.  
Figure 3 shows the vector magnitude for each acceleration. Vector magnitude was chosen as the means to determine the intensity of the vector formed by the accelerometer in different phases. It can be observed that there are significative differences in terms of amplitude for all three axis accelerations. While we have high amplitudes for fast walk in each session, in the case of resting, there were small changes in the position of the smartwatch. Additionally, in Figure 3, there are major differences between fast walk and the other activities, but the similarities in terms of amplitude between slow walk and climbing up stairs require further investigation.  
If the data from the accelerometer are plotted in tridimensional space, as in Figure 4, it can be observed that the dispersion of points for each activity have different concentration points. This is the best representation for accurately spotting significant differences between the selected activities. While an overlap between the different points of the activities can be observed, these are a result of the nature of movement. The main insight they provide is that all four activities form different data clusters in three-dimensional space. The most significant observation is that for fast walk, we have a more spread-out pattern. Another  
Figure 2: Accelerations for each activity.  
meaningful observation is that in 3D space, the points for resting are not what we expected: there is less variation compared with the other activities across all three axes, and they are concentrated in a small region.  
Another source which can be used for features extraction is the orientation sensor. After computing [PERSON]'s angles, the differences between slow walk and climbing up stairs led to a new hypothesis: in conjunction with accelerations, it is possible to create a data model which can easily predict a performed action. This hypothesis is based on Figure 5. It can be observed that for resting, where the movement is minimal, the three angles are  
Figure 4: Three-dimensional representation of activities.  
Figure 3: Vector magnitude for each acceleration.  
relatively constant, meaning that the person carried out small body movements during the recording session. The most recognizable activity is climbing up stairs, which has high variations in terms of amplitude for roll and yaw. This is because when a person is climbing up stairs, there is more of a vertical motion compared with that involved in fast walk or slow walk, where the movement takes place along a horizontal plane and resembles the swing of a pendulum.