## 4 Evaluation and Analysis
### Axis Analysis of Activities
* **Downstairs**: Figure 9d depicts that the periodic patterns are slightly similar to the Figure 9b. Each small peak represents movement down a single stair. The z-axis values show a similar pattern showing negative readings, reflecting the regular movement down each stair which was positive in the previous case (upstairs). While walking downstairs, downward motion can be seen as light blue strides which show a negative axis while the forward motion can be seen in purple strides and red lines, showing horizontal movements that are stable in the start but suddenly it shows a high read peak which turns a mentioned earlier in the dataset collection section. The range of x-axis falls between \([-11,13]\), y-axis between \([-12,-3]\) and the range of z-axis lies between \([-10,15]\), which is quite similar to the Upstairs. In Table 3, it is shown that the recognition rate of upstairs activity against \(x-y\) axis is 70.7%, \(x-z\) axis is 68.5%, \(y-z\) axis is 87.1%, and \(x-y-z\) is 71.5%. Hence for this activity y-axis and z-axis of the accelerometer sensor are the potential features. * **Sitting**: Figure 9e depicts that all reading against each axis is, constant and stable patterns without any regular periodic pattern. This activity shows a unique behavior. By looking at accelerometers detected outputs, we observe that the range of x-axis lies between \([-1,0]\), y-axis value \([-1,1]\) and the value of z-axis lies between \([9,11]\). In Table 3, it is shown that the recognition rate of upstairs activity against \(x-y\) axis is 90.2%, \(x-z\) axis is 86.8%, \(y-z\) axis is 94.7%, and \(x-y-z\) is 9.0%. Hence for this activity, y-axis and z-axis of the accelerometer sensor are the potential features. * **Standing**: This activity also shows a unique behavior as depicted in Figure 9f. It is seen that accelerometer readings are constant and depicts stable patterns without any regular periodic pattern. The range of x-axis lies between \([0,2]\), y-axis value \([-9,-11]\) and the value of z-axis lies between \([-1,1]\). In Table 3, it is shown that the recognition rate of upstairs activity against \(x-y\) axis is 90.1%, \(x-z\) axis is 88.2%, \(y-z\) axis is 96.4%, and \(x-y-z\) is 93.7%. Hence, for this activity y-axis and z-axis of the accelerometer sensor are the potential features. Besides our approach, authors in [52] used principal component analysis (PCA) for feature selection from fall detection datasets. This dataset contains 3 accelerometer Axis as features. According to the authors, PCA selects optimal features from the feature matrix. PCA measures the importance of a feature based on variance. Feature having high variance are treated as principal components and low variance feature are considered as noise. In this study, we focus on daily life six activities presented in Section 4.3. Applying PCA on our dataset provides the X-axis and Y-axis of the accelerometer as a principal component but provides an overall accuracy of 85.4% which is far less than our approach as shown in Table 3. The rationale behind this is that activities such as standing, sitting, walking and jogging can be recognized efficiently using x and y-axis but not other complex activities such as upstairs and downstairs. These activities are efficiently recognized with the accuracy of 93.3% using y and z-axis as shown in Table 3.  
To sum up all the discussion, observations and analysis, we strongly claim to use the y-axis and z-axis of the accelerometer sensor to recognize activities: _Walking_, _Standing_, _Sitting_, _Upstairs_, _Downstairs_ and _Jogging_ experimented extensively in this paper.