## 4 Evaluation and Analysis
### Axis Analysis of Activities  
In this section, we analyze the effectiveness and contribution of each axis of the tri-axial accelerometer sensor to improve the recognition rate. We choose six daily life activities: _upstairs, downstairs, sitting, walking, jogging, and standing_ in this work. The experiment results of this analysis are carried through the three-fold cross-validation. These activities strongly depict the human physical routine. The x-axis in each graph depicts the horizontal movement [+x, -x] of the leg, y-axis depicts the motion in upward and downward directions [+y, -y] while z-axis depicts the forward and backward motion [+z, -z] of the leg. The acceleration plots for the Six Activities (a-f) shown below in Figure 9 represents the acceleration and time of all the activities. Red curves are showing the x-axis of the accelerometer, blue lines are showing the y-axis of the accelerometer and the purple line are showing the z-axis of the accelerometer. The x-axis of the graphs shows the time in seconds, is accessed while the y-axis of the graph shows the acceleration of each sensor ranging between [-20,20]. Below we show the behavior of each activity according to each axis. * **Walking**: Figure 9a depicts that the periodic pattern of walking activity is quite similar to Figure 9b. While walking upward and downward periodic motion can be seen as light blue strides while the forward motion can be seen in purple strides and red lines showing horizontal movement is stable. The value of the x-axis lies between [-12, 12], y-axis value [-18, 5] & the value of z-axis lies between [-9, 16] which is quite unique. These observations can be proven by Table 3 and Figure 10 depicting a combination of the different axis of the accelerometer to recognize the activities. In Table 3 the recognition rate of walking activity against \(x-y\) axis is 89.5%, \(x-z\) axis is 90.0%, \(y-z\) axis is 95.0%, and \(x-y-z\) is 92.5%. Hence, for walking activity, the y-axis and z-axis of the accelerometer sensor are the potential features. * **Jogging**: Figure 9b depicts periodic patterns quite similar to the Figure 9a. Upward and downward motion can be seen as light blue strides while the forward motion can be seen in purple strides which are quite high than walking and red lines showing horizontal movements are stable. In Table 3, it is shown that the recognition rate of jogging activity against \(x-y\) axis is 92.2%, \(x-z\) axis is 94.1%, \(y-z\) axis is 96.5%, and \(x-y-z\) is 94.0%. Hence for this activity y-axis and z-axis of the accelerometer sensor are the potential features. By looking at accelerometers detected readings, it shows that the range of x-axis falls between [-8, 10], y-axis value [-20, 18] and the value of z-axis lies between [-10, 14]. In the previous case, the same type of results are obtained which further strengthen our observations. Figure 8: Confusion matrix of activity recognition using MLP classifier. * **Upstairs**: The patterns in Figure 9c are quite similar to the Figure 9b of jogging. While going upstairs, upward and downward motion can be seen as light blue strides while the forward motion can be seen in purple strides and red lines showing horizontal movements are stable. This activity is getting  
Figure 9: Acceleration plots of daily life physical activities. confused with jogging activity as the only difference between both is of the z-axis. There is a high upward positive peak and downward negative peak after a specific periodic interval which depicts that a user stepped up a stair and then suddenly it goes to the initial stage. The range of x-axis lies between \([-12,10]\), y-axis between \([-4,16]\) and z-axis lies between \([-11,13]\), which is quite similar to the jogging. In Table 3, it is shown that the recognition rate of upstairs activity against \(x-y\) axis is 79.6%, \(x-z\) axis is 65.5%, \(y-z\) axis is 89.8%, and \(x-y-z\) is 79.3%. Hence, for this activity, y-axis and z-axis of the accelerometer sensor are the potential features.