Human Activity Recognition based on LSTM Neural Network Optimized by PSO Algorithm

Abstract

Human activity recognition (HAR) approaches play significant roles in understanding behavioral activities. These tools allow personalized services and support for users in varied circumstances. Hence accurate prediction of user activity is valuable for several applications such as elderly care, healthcare, sports, and smart homes. In recent times, machine learning with deep learning models, particularly Convolutional Neural Networks (CNN), are becoming popular for human activity recognition. However, our earlier studies have proved that Recurrent Neural Networks (RNN) produce superior results to CNN. In the current study, a new approach, Long Short Term Memory(LSTM) neural network and Particle Swarm Optimization (PSO) algorithm are proposed. Though LSTM models produce good results in HAR, there are few parameters to consider for tracking down the optimal LSTM model. Therefore PSO algorithm is utilized for the optimization of the parameters. It possesses a quick convergence rate and the ability to enhance the prediction accuracy of LSTM in contrast to popular methods. After the training of the LSTM model, the weights are further optimized by using the PSO algorithm and are substituted into the model during the verification stage. This study investigates the performance of the LSTM-PSO model compared with few recurrent neural networks. For the implementation of the model, a benchmark WISDM dataset is utilized. The simulation results indicate that the proposed approach refines the recognition rate up to 97% which exceeds those of the compared models.