Particle Swarm Optimization of Convolutional Neural Networks for Human Activity Prediction

The increased usage of smartphones for daily activities has created a huge demand and opportunities in the field of ubiquitous computing to provide personalized services and support to the user. In this aspect, Sensor-Based Human Activity Recognition (HAR) has seen an immense growth in the last decade playing a major role in the field of pervasive computing by detecting the activity performed by the user. Thus, accurate prediction of user activity can be valuable input to several applications like health monitoring systems, wellness and fit tracking, emergency communication systems etc., Thus, the current research performs Human Activity Recognition using a Particle Swarm Optimization (PSO) based Convolutional Neural Network which converges faster and searches the best CNN architecture. Using PSO for the training process, intends to optimize the results of the solution vectors on CNN which in turn improve the classification accuracy to reach the quality performance compared to the state-of-the-art designs. The study investigates the performances of PSO-CNN algorithm and compared with that of classical machine leaning algorithms and deep learning algorithms. The experiment results showed that the PSO-CNN algorithm was able to achieve the performance almost equal to the state-of-the-art designs with a accuracy of 93.64%. Among machine learning algorithms, Support Vector machine found to be best classifier with accuracy of 95.05% and a Deep CNN model achieved 92.64% accuracy score