# Internet of Things for Sensing: A Case Study in the Healthcare System
## 6 Results and Discussion
### Classification Results  
#### 6.1.1 Results Obtained Using SVM  
As discusses in an earlier section, the proposed method utilizes two parameters for evaluation: the variances of amplitude information and calibrated phase information when the subject performs various activities. We adopted the one-versus-rest approach and trained the SVM classifier using built-in function in MATLAB called \"svmtrain\" and obtained the optimal decision boundary by utilizing the sequential minimal optimization solver. In order to train the SVM, 40, 80, and 120 samples from each class were used separately. The 10 SVM features extracted from training data are indicated  
Figure 4: Time history of person’s five different activities.  
in Table 2. Each one of them describes the measurement per CI packet distinctively. The results in this particular table indicate the percentage accuracy, which is one of the six performance metrics used in this paper. We have explored the remaining metrics along with accuracy in a later part of this paper. The data classification results using the SVM algorithm are described in Table 3 using microwave absorbing material and without using microwave absorbing material, respectively. The first column in Table 3 indicates the type of SVM kernel function used for training data set, and the second column denotes the training samples (40, 80 and 120). The remaining five columns \(a\), \(b\), \(c\), \(d\), \(e\) representing the sitting, walking, push-ups, squatting, and sleep attacks describes the percentage accuracy. The results indicate that when the linear kernel function was used to differentiate the five human activities from 40 training samples, the percentage accuracy is 98.75% for sitting, 78.25% for walking, 98.25 for push-ups, and 65% for sleep attacks. Similarly, the accuracy rate is around 65% for sleep attacks using linear kernel function when 80 and 120 training samples were considered, as shown in Table 3. An increase in the training dataset marginally improved the accuracy rate for sleep attacks but remained around 65%. However, the percentage accuracy greatly improved when the ten SVM features were considered using linear kernel function. The accuracy is between 75.25% and 98.5% for 40 training samples, 78.2% to 98.75% for 80 training samples, and 78% to 99% for 120 training samples. The accuracy rate of sleep attacks detection for 40, 80, and 120 training samples is 84%, 87%, and 89.75%, respectively, as shown in Table 3. The polynomial kernel function worked better than linear kernel function. The error rate is between 69.25% and 98.5% for 40 training samples, 71.5% to 99.5% for 80 training samples, and 74.25% to 99% for 120 training samples when the polynomial kernel function was used as far as five SVM features were considered. The accuracy rate obtained for the RBF kernel function is between 74% and 98% when five SVM featured were used, as shown in Table 3. However, the polynomial kernel function and RBF kernel function provided the best results when 10 SVM features were used. The accuracy rate obtained using polynomial kernel function considering ten SVM features is between 81% to 98.5% for 40, 80, and 120 training samples, respectively. While the percentage accuracy is between 78.75% and 98.75% when 10 SVM features were used, considering the RBF kernel function. However, we observe a slight decrease in percentage accuracy when the experiment was performed without microwave-absorbing material. The results obtained are shown in Table 3. Table 3 indicates the comparison between the three SVM kernel functions used for transforming the data. We observe that the accuracy rate is decreased by 5-7% without deploying microwave-absorbing material in the indoor environment. We further investigate the results obtained using KNN and RF algorithms.