## 2 Background
### Feature Selection Approaches Based on Particle Swarm Optimization
Machine Learning Approaches from the Literature That Consider the Daily Life Activities (DaLiAc) Dataset  
The Daily Life Activities (DaLiAc) dataset was introduced in [16] as a benchmark dataset that is analyzed in the context of a hierarchical classification system for DLAs. Compared to the method presented in this article, which evaluates the performance of the DLAs classification using 10-fold cross validation, the method presented in that article considers a leave-one-subject-out procedure. The authors also address the challenge of a high number of features, which leads to a very high computational complexity in the case of embedded systems or real time applications and they even suggest the reduction of the features, giving, as an illustrative example, the sequential feature selection. The authors of [30] use the DaLiAc dataset as experimental support for a method based on a classifier ensemble of randomized trees, and their objective is to maximize the recognition performance, both in the replacement scenario and in the relocation scenario. Compared to the approach presented in this article, they used more \(K\)-folds cross-validation schemes with \(K\) from \(\{2,3,5,10,20\}\) and they also used the accuracy to measure the performance. They also included junk data in their experiments and therefore their results are not directly comparable with the results obtained in this article. The human activity recognition is approached in [31] by combining a number of classifiers, and the method presented in that article, which is based on a combination of models outperforms other types of classifiers combination models. However, the challenge that still remains is how to increase the robustness of that method against interperson variability. The method based on BPSO described in this article addresses that challenge partially by assigning a weight to each monitoring sensor and consequently the weights are different and adapted for each monitored subject. The algorithms applied in [32] for improving the accuracy of activity recognition are k-NN, logistic regression (LR), naive bayes (NB), random forest (RF), extremely randomized trees (ERT) and SVM. The method described in that article consists of four phases, namely segmentation, feature extraction, feature selection and evaluation. In the case of the DaLiAc dataset, the overall mean classification is 89.6%. The work described in this article is inspired from the work from that article as follows: (1) our work considers the selection of the features according to the positions of the sensors that generate them, (2) it considers the RF algorithm, and (3) it applies the proposed research method on several datasets. However, one weakness of that work is that it does not address the importance of each monitoring sensor, even though it addresses the optimal number of sensors, the positions of the sensors, and the optimal number of features for each sensor. The method presented in [33] considers the DaLiAc dataset from a different perspective than the other articles, which consider it as experimental support, converting the inertial sensor signals to images and then applying a convolutional neural network (CNN) [34] for images classification. Even if that method leads to better classification results than the traditional classification methods, it is also subject to the limitations of CNNs, such as the class imbalance, the overfitting and poor performance when the input data is small. Moreover, CNNs are also computationally expensive and, in this article, that challenge is addressed by trying to reduce the number of features so that the classifiers can be trained faster and the classification results are optimized.