It is possible to compare results obtained using monolithic classifiers and MCS, the best result for Data Set 1 is on Table 20 and for Data Set 2 is on Table 21. Analyzing the data sets separately, the results are quite similar, but for Data Set 1 the MCS using KNORA-U with Perceptron was better than the best monolithic classifier using MLP. Besides, both results were better the SVM result presented in previous work (Anguita et al.,2013). For Data Set 2, the monolithic model was better than MCS one.
Table 20: Best results from monolithic and MCS for Data Set 1 Model Accuracy Precision Recall F1 score
MLP 0.9477 0.9513 0.9477 0.9479
KNORA-U
(Perceptron) 0.9562 0.9565 0.9562 0.9561
Table 21: Best results from monolithic and MCS for Data Set 2 Model Accuracy Precision Recall F1 score Random Forest 0.8804 0.8780 0.8780 0.8780 Static Selection
(Decision Tree) 0.8398 0.8453 0.8398 0.8398
Comparing all results for both data sets is interesting to notice that models were better in Data Set 1. As Table 4 shows, that was expected, because Data Set 1 has more instances than Data Set 2, the first has 10299 instances, and the second has 5744 instances. Another aspect of analyzing is that using MCS does not achieve a better result than monolithic classifiers. Table 22 shows the result obtained by (Anguita et al.,2013) and the best results obtained for the Data Set 1, so it is possible to see that the results are similar to those obtained by (Anguita et al.,2013).
Table 22: Best results for Data Set 1 and results obtain by (Anguita et al.,2013) Model Accuracy Precision Recall
SVM
(Anguita et al.,2013) 0.9600 0.9600 0.9600 KNORA-U
(Perceptron) 0.9562 0.9565 0.9562
5
CONCLUSION AND FUTURE WORKS
Physical Inactivity brings some health problems, and the decreasing of active time per person is one of the biggest concerns of the WHO. One of the initial efforts to treat it, it is to identify and measure the activity performed by someone and HAR is the tools to it. HAR process is divided into two steps: the acquisition phase, that uses a sensor to collect information about the movement performed by someone and the classification phase, that consists in classify the data in one of the ADL.
The presented work tested some monolithic classifiers and some MCS, comparing their results using accuracy, precision, recall, and F1-score. Also, this work compared the results of the monolithic classifiers using L2 distance normalization and MinMaxScaler in HAR with no normalized data, showing how these techniques impact in the results of some classifiers as SVM and Bernoulli NB. Considering these worse result, the MCS were trained using no normalized data.
Analyzing the results, it is notable that the MCS does not achieve better results than the monolithic classifiers. Even in Data Set 1 that the best model, KNORA-U using Perceptron, is only quite better than the best monolithic classifier, MLP, the KNORA-U obtain an accuracy of 0.9562, and the MLP an accuracy of 0.9477. In Data Set 2, the best monolithic classifier, Random Forest, achieve an accuracy of 0.8804, better than the accuracy of 0.8398, accomplish by the best MCS, Static Selection with Decision Tree.
Besides these results, some points can be made in future works to complete the analyzes on HAR using MCS:
Compare the computational time of training all the models, to give the trade-off of use MCS instead of using a monolithic classifier.
Perform a grid search on the best parameters for some classifiers, as MLP, SVM and the MCS to find the finest results for these classifiers.
Use both data sets to train a unique model, given more instances in the training phase and observe if that leads to better results.
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Compare achieved results using a statistical test to precisely define which is better between monolithic classifiers and MCS.
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