## 7 Experimental Results  
### Training and Prediction Results  
The residual neural network described in Section 5.4 is trained for 8, 16, 24, 32, 40, 48, 64, and 80 filters per convolution. It is then quantized using the methods described in Section 6.1. Results are averaged over 15 runs for each number of filters.  
The results for the original 32-bit floating-point model (UCA-EHAR float32), the 16-bit fixed-point quantized model with post-training quantization (UCA-EHAR 16-bit PTQ) and the 8-bit fixed-point quantized model with post-training quantization (UCA-EHAR 8-bit PTQ) are shown in Figure 5 and reported in Appendix B for each number of filters per convolution. As can be seen, 16-bit fixed-point quantization does not cause any accuracy loss while 8-bit fixed-point quantization causes up to 2.4% accuracy drop.  
Concerning the memory used by the parameters, Figure 6 shows that the 16-bit fixed-point model is the most efficient, using half the memory of the 32-bit floating-point model but without any loss of accuracy. On the other hand, the 8-bit fixed-point model is less efficient than the 32-bit floating-point model since a noticeable loss of accuracy can be observed.  
Figure 5: Accuracy vs. filters.  
The confusion matrix, shown in Figure 7 and extracted from one training for 80 filters per convolution, highlights the difficulty for an artificial neural network to differentiate the SITTING and STANDING activities from the collected data. The reason is that the orientation of the smart glasses remains the same for both classes, and the signals mostly stay constant for both of these motionless activities as seen in Figures A3 and A4 of Appendix C. It can be noted that the same confusion was already observed on existing datasets such as UCI-HAR.  
Figure 6: Accuracy vs. parameters memory.  
Figure 7: Confusion matrix for 80 filters per convolution.  
An evaluation per subject has also been performed and is reported in Figure 8. The training set and the parameters are the same as the one used for the previous confusion matrix. However, inference is evaluated using each subject of the testing set one by one. It is important to note that since the classes are unbalanced, the accuracy in the \"TOTAL\" column does not represent the average of each class's accuracy. Instead, it is the accuracy over all the test vectors of a given subject, and classes with more test vectors will have a greater influence on the resulting percentage of correct predictions. For example, for subject T20 the \"TOTAL\" of 75% is the most influenced by the \"STANDING\" activity, having much more samples than other activities and bringing the accuracy down. The same applies for the \"TOTAL\" line, since subjects do not all have the same number of test vectors per class. The bottom right cell, at the intersection of the \"TOTAL\" line and the \"TOTAL\" column, represents the accuracy over the entire testing set. Results show a discrepancy between subjects for some activities such as WALKING_DOWNSTAIRS, WALKING_UPSTAIRS and DRINKING, while other activities are more homogeneous. The STANDING activity, however, is hard to classify for all subjects. The reason is a large confusion with the SITTING activity, as previously shown in the confusion matrix.