# Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification
## 1 Introduction  
Nowadays, mobile devices in all everyday tasks are increasing, and their usage allows users to stay connected and communicate with ease [1, 2]. The current pandemic situation discourages social interaction and personal contacts, enhancing the role of technology in promoting social distancing while being connected [3, 4] and active, avoiding sedentary positions [5, 6]. Several studies use mobile devices to identify human activities and create a personal agenda to track people [7, 8, 9, 10]. This is especially important for people with special needs, including older adults or people with chronic diseases [11, 12, 13]. The constant contact with professional healthcare will benefit people's quality of life [14, 15, 16]. Sensors are vital for data acquisition related to human activities [17, 18, 19] and, lately, even for diagnostic purposes [20, 21]. Mobile devices include a large variety of sensors, including accelerometer, magnetometer, gyroscope, acoustic, location, contacts, and other types of sensors [22, 23]. The development of monitoring solutions is recurrent in the literature, and the advances in this type of device promoted the creation of intelligence solutions [24; 25; 26; 27; 28; 29; 30]. The data acquired by sensors depends on the mobile devices' positioning, and their processing is sometimes difficult [31; 32]. The data normalization may be powerful in the data processing stage without a large increase in memory and power processing needs [33]. There are different data normalization methods that may be implemented, but the focus of this study was related to the implementation of MaxAbsScaler [34], MinMaxScaler [35], RobustScaler [36], and Z-Score [33] normalizers. Finally, for the data processing, several types of machine learning methods may be implemented, including support vector machine (SVM) [37], decision tree [38], AdaBoost [39; 40], artificial neural networks (ANN) [41], k-nearest neighbor (kNN) [42], combined nomenclature (CN2) rule inducer [43], and stochastic gradient descent (SGD) [44]. However, this paper's remainder proposes studying the parameters related to deep neural networks (DNN) [36]. This study aims to explore the use of the \"Heterogeneity Activity Recognition Data Set\" [2] for the implementation of four data normalization techniques, for further implementation of data classification techniques for the automatic recognition of human activities. Furthermore, what this study aims to achieve is to find out if the used normalization technique influences the human activity recognition performance. The implemented method was based on deep neural networks (DNN) to classify the different data included in the dataset. Furthermore, the data classification results are related to the data normalization methods implemented and compared with the previous studies with other datasets [45; 46; 47]. There are many research studies related to the recognition of human activities. Still, it is not possible to say which artificial intelligence method is reliable in general, for any given dataset. This study's scope consists of using data acquired from inertial sensors embedded in a mobile device, including smartphones, smartwatches, and tablets, to implement data normalization and data classification techniques to identify human activities. One lack of previous studies was related to the data acquisition, in that the data were always acquired with the same device, i.e., a BQ Aquaris 5.7 smartphone [48; 49]. Previous studies were performed with accelerometer, magnetometer, and gyroscope sensors with the same classification technique implemented in this study [46; 50; 51]. The normalization technique previously implemented was the mean and standard deviation, like the Z-Score normalizer application. The previously used dataset is available at [52]. The previously used dataset has one more human activity than the dataset used in this study. The previous studies used data fusion techniques to merge the accelerometer, magnetometer, and gyroscope sensors' data.