Human Activity Recognition Using Parallel Cartesian Genetic Programming
Bruno M. P. Silva, Heder S. Bernardino, Hélio J. C. Barbosa · 2021
Human activity recognition (HAR) is applicable to a wide range of real-life situations. While machine learning algorithms can be applied for solving this problem, difficulties remain, such as handling a large amount of data available for training and selecting the most appropriate features. Hence, the advent of methods to reduce these issues and improve the currently available algorithms is relevant. Thus, we propose here the application of Cartesian Genetic Programming of Artificial Neural Networks (CGPANN) for training models for HAR. As the computational cost is a relevant issue in this context, high-performance computing strategies in graphic processing units (GPU) are proposed for CGPANN. Two computational experiments are executed and the results show a decrease in computational time spent with the usage of different data structures for the parallel CGPANN on the GPU. Moreover, the CGPANN models for HAR are promising when compared to results from the literature.