# Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification
## 4 Discussion and Conclusions  
The \"Heterogeneity Activity Recognition Data Set\" [2] was acquired with different mobile devices, including smartphones, tablets, and smartwatches. Different devices have different frequencies of data acquisition. We experimented with four normalization techniques: MaxAbsScaler, MinMaxScaler, RobustScaler, and Z-Score. Furthermore, the DNN method was implemented for the classification of the different human activities. This study analyzed the difference between non-normalized and normalized data, verifying that the dataset used in this study revealed the best results with the non-normalized data. However, the previously used dataset revealed the best results with normalized data. The results showed that the best accuracy (81.46%) was reported with non-normalized gyroscope data to recognize three human activities. Furthermore, 79.11% accuracy was obtained with the use of accelerometer data to recognize three human activities, also with non-normalized data. On the contrary, the previously used dataset revealed the best accuracy with normalized data with or without data fusion techniques. Based on the comparison of the previous results presented in Table 11, non-normalized data reported better accuracy with the dataset analyzed in this study than the previously used dataset. However, normalization techniques reported bad accuracy with this dataset compared to the previously used dataset which reported the correct recognition of five human activities. This dataset only reported a maximum of three human activities correctly. As future work, the impact of different techniques for data classification, data imputation, and data normalization should be explored, as well as their impact when processing multi-modal data collected by various sensors. Furthermore, other data normalization techniques should be evaluated, as well as how the subsequently used machine learning algorithms benefit from the normalization. As this research shows, deep learning algorithms can overcome bias in the data without normalization. Furthermore, when processing homogeneous data collected by mobile devices, with completely identical data collection frequencies and different ranges of data, this research shows that data normalization impairs the classification accuracy. Other studies [56; 57] show that more classical algorithms, such as SVMs, decision trees, and tree ensembles, considerably benefit from data normalization. These algorithms need to be further evaluated with the proposed approaches. In the future, the impact of the presented data normalization and imputation methods should also be evaluated on other datasets. In particular, when using other sensors in collecting multi-modal data from various sensors, such as microphones [58], pressure sensors, infrared sensors, proximity sensors, and oximeters, we expect the impact of the proposed data normalization algorithms to be even more emphasized. In conclusion, the benefits of the implementation of data normalization techniques depend on the dataset. It is unclear if normalization would improve the data classification because the number of samples used was smaller than the previously used dataset. As the dataset is unbalanced, it may also influence the implementation of artificial intelligence methods for activity recognition. Conceptualization, methodology, software, validation, formal analysis, investigation, writing--original draft preparation, writing--review and editing; I.M.P., F.H., N.M.G., P.L. and E.Z. All authors have read and agreed to the published version of the manuscript. This work is funded by FCT/MEC through national funds and co-funded by FEDER--PT2020 partnership agreement under the project **UIDB/50008/2020** (_Este traballo e franaciado pela FCT/MEC atraves de fundos nacionais e cofinanciado pel EDER, no albutio do Acordo de Parcia PT2020 no albutio do projeto UIDB/50008/2020_). This work is also funded by National Funds through the FCT-Foundation for Science and Technology, I.P., within the scope of the project **UIDB/00742/2020**.