New machine learning approaches for real-life human activity recognition using smartphone sensor-based data.

In recent years, mainly due to the application of smartphones in this area, research in human activity recognition (HAR) has shown a continuous and steady growth. Thanks to its wide range of sensors, its size, its ease of use, its low price and its applicability in many other fields, it is a highly attractive option for researchers. However, the vast majority of studies carried out so far focus on laboratory settings, outside of a real-life environment. In this work, unlike in other papers, progress was sought on the latter point. To do so, a dataset already published for this purpose was used. This dataset was collected using the sensors of the smartphones of different individuals in their daily life, with almost total freedom. To exploit these data, numerous experiments were carried out with various machine learning techniques and each of them with different hyperparameters. These experiments proved that, in this case, tree-based models, such as Random Forest, outperform the rest. The final result shows an enormous improvement in the accuracy of the best model found to date for this purpose, from 74.39% to 92.97%. • This paper presents a comparison of the main machine learning algorithms for HAR. • A dataset taken in a real-life environment was used, unlike in other studies. • Experiments were done to get the best model configurations for long-themed activities. • This work also reviews the gyroscope's real influence to the final results. • The current approaches in this field, oriented towards real life, were highly improved. [ABSTRACT FROM AUTHOR]