**4. Results**

#### *4.1. Identification of the Environment of the Activities of Daily Living with Microphone*

The implementation of MLP with Backpropagation reported the results presented in Figure 2, verifying that the accuracy reported is very low with all datasets. With non-normalized data (Figure 2a, the results achieved are between 10% and 15%. With normalized data (Figure 2b, the results obtained are between 10% and 20%, where the best results are achieved with dataset 1.

**Figure 2.** Results obtained with Multilayer Perceptron (MLP) with Backpropagation for the different datasets of microphone data. (**a**) shows the results with non-normalized data. (**b**) shows the results with normalized data.

Moreover, the results reported by the implementation of the FNN with Backpropagation are presented in Figure 3. In general, this implementation reports better results with non-normalized data. With non-normalized data (Figure 3a), the FNN reports results higher than 70% with dataset 1 with a maximum number of training iterations, dataset 2 with 10<sup>6</sup> of training iterations, and dataset 4 with 4 × 10<sup>6</sup> of training iterations. With normalized data (Figure 3b), the FNN reports results below than 60% but the results achieved are higher than 60% with the dataset 4 trained over 10<sup>6</sup> and 2 × 10<sup>6</sup> of iterations.

**Figure 3.** Results obtained with Feedforward Neural Network (FNN) with Backpropagation for the different datasets of microphone data. (**a**) shows the results with non-normalized data. (**b**) shows the results with normalized data.

The results of the implementation of DNN are presented in Figure 4, where, with non-normalized data (Figure 4a), the results obtained are below 20% with datasets 1 and 2, and the results obtained are higher than 40% with datasets 3 and 4. In addition, with normalized data (Figure 4b), the results reported are round 50% with all datasets.

**Figure 4.** Results obtained with Deep Neural Network (DNN) for the different datasets of microphone data. (**a**) shows the results with non-normalized data. (**b**) shows the results with normalized data.

In Table 4, the maximum accuracies achieved with the different implementations of ANN are related to the different datasets used for the microphone data and the maximum number of training iterations, verifying that the best results are achieved with the FNN with Backpropagation with non-normalized data.


**Table 4.** Best accuracies obtained with the different frameworks, datasets and number of iterations for the recognition of environments using microphone data.

In conclusion, the method for the recognition of the environment that should be implemented in the framework for the recognition of ADL and their environments is the FNN with Backpropagation using non-normalized data, because it achieves results around 86.50% with the dataset 1.

#### *4.2. Identification of the Standing Activities with the Environment Recognized and the Accelerometer Sensor*

The use of normalized data resulted in the achievement of an accuracy of 100% with MLP with Backpropagation, FNN with Backpropagation and DNN methods, because the use of the correct recognition of environments with acoustic data provides a correct discretization of the accelerometer data.

Following the use of non-normalized data, Figure 5 shows the results obtained with MLP with Backpropagation, FNN with Backpropagation and DNN methods. MLP with Backpropagation (Figure 5a) reported results between 50% and 100%, where the better accuracy was achieved with the datasets 1 and 4. FNN with Backpropagation (Figure 5b) reported results around 100%, except with dataset 1 that achieves an accuracy around 50%. DNN method (Figure 5c) reported results around 100% with datasets 2, 4 and 5 with all training iterations, and with dataset 3 with 4 × 10<sup>6</sup> iterations, but the results obtained with other combinations are below expectations.

**Figure 5.** Results obtained with MLP with Backpropagation (**a**), FNN with Backpropagation (**b**) and DNN (**c**) methods for the different datasets of environment and accelerometer data.

In Table 5, the maximum accuracies achieved with the different types of ANN are presented with the relation of the different datasets used for the environment recognized and the accelerometer data and the maximum number of iterations.


**Table 5.** Best accuracies obtained with the different frameworks, datasets and number of iterations for the recognition of standing activities with the accelerometer data and the environments recognized.

Regarding the results obtained, in the case of the use of the environment recognized and the accelerometer data in the module for the recognition of standing activities in the framework for the identification ADL and their environments, the implementation that should be used is a DNN with normalized data because the results obtained are always 100%.

#### *4.3. Identification of the Standing Activities with the Environment Recognized and the Accelerometer and Magnetometer Sensors*

The use of normalized data resulted in the achievement of an accuracy of 100% with MLP with Backpropagation, FNN with Backpropagation and DNN methods, because the use of the correct recognition of environments with acoustic data provides a correct discretization of the accelerometer and magnetometer data.

Following the use of non-normalized data, Figure 6 shows the results obtained with MLP with Backpropagation, FNN with Backpropagation and DNN methods. MLP with Backpropagation (Figure 6a) reported results around 100%, except with the datasets 1 and 5 which achieved an accuracy around 50%. FNN with Backpropagation (Figure 6b) reported results around 100%. DNN method (Figure 6c) reported results around 100% with dataset 5 with all training iterations, and with dataset 4 with 10<sup>6</sup> of training iterations, but the results obtained with other combinations are below expectations.

**Figure 6.** Results obtained with MLP with Backpropagation (**a**), FNN with Backpropagation (**b**) and DNN (**c**) methods for the different datasets of environment and accelerometer and magnetometer sensors' data.

In Table 6, the maximum accuracies achieved with the different implementations of ANN are presented with the relationship between the different datasets used for the environment recognized, and the accelerometer and magnetometer sensors' data, and the maximum number of iterations.


**Table 6.** Best accuracies obtained with the different frameworks, datasets and number of iterations for the recognition of standing activities with the accelerometer and magnetometer data, and the environments recognized.

DNN with normalized data always reported results equal to 100% with the use of the accelerometer and magnetometer sensors' data combined with the environment recognized. Thus, the framework for the identification ADL and their environments should implement the DNN with normalized data.

#### *4.4. Identification of the Standing Activities with the Environment Recognized and the Accelerometer, Magnetometer and Gyroscope Sensors*

On the one hand, the results reported by the implementation of the MLP with Backpropagation using the MLP with Backpropagation are presented in Figure 7. With non-normalized data (Figure 7a), the results achieved are around 100%, except with the datasets 1 that achieves an accuracy around 50%. With normalized data (Figure 7b), the results obtained are always around 100% with all datasets.

**Figure 7.** Results obtained with MLP with Backpropagation for the different datasets of environment, and accelerometer, magnetometer and gyroscope sensors' data. (**a**) shows the results with non-normalized data. (**b**) shows the results with normalized data.

On the other hand, the results reported by the implementation of the FNN with Backpropagation are presented in Figure 8. With non-normalized data (Figure 8a), the results achieved are always around 100%. With normalized data (Figure 8b), the results obtained are always around 100% with all datasets.

**Figure 8.** Results obtained with FNN with Backpropagation for the different datasets of environment and accelerometer, magnetometer and gyroscope sensors' data. (**a**) shows the results with non-normalized data. (**b**) shows the results with normalized data.

Additionally, the results reported by the implementation of DNN are presented in Figure 9. On the one hand, with non-normalized data (Figure 9a), the results obtained are around 90% with dataset 5 with all training iterations. However, the results obtained with other datasets are below the expectations. On the other hand, with normalized data (Figure 9b), the results obtained are always around 100% with all datasets.

The datasets acquired from the accelerometer, magnetometer and gyroscope combined with the environment recognized, the maximum number of iterations and the maximum accuracies reported by the different implementations of ANN are presented in Table 7.

Using the environment recognized and the accelerometer, magnetometer and gyroscope sensors' data in the module for the recognition of standing activities in the framework for the identification ADL and their environments, the reported results are always 100% with implementation of DNN with normalized data.

**Figure 9.** Results obtained with DNN for the different datasets of environment, and accelerometer, magnetometer and gyroscope sensors' data. (**a**) shows the results with non-normalized data. (**b**) shows the results with normalized data.

**Table 7.** Best accuracies obtained with the different frameworks, datasets and number of iterations for the recognition of standing activities with the accelerometer, gyroscope and magnetometer data, and the environments recognized.

