Human activity recognition using deep learning techniques with spider monkey optimization

The human activity recognition (HAR) system recognizes human actions in daily life. There is a need for HAR to build a smart home and an intelligent healthcare environment. HAR is challenging, considering the complexity and heterogeneity of sensors used to recognize it. Deep learning models are the one area where the researcher applies to recognize the activities. However, effective feature engineering and optimization methods help improve the recognition model’s performance. In this work, Spider Monkey Optimization is applied for training the deep neural network. UCI HAR, WISDM, KTH action and PAMAP2 datasets are used to evaluate the proposed system. The dataset has the activities like walking, standing, lying, jogging, stair-up and stair-down activities. Here, the spider monkey model’s fitness function is initialized in the hidden layer of the Recurrent Neural Network to enhance accuracy and precision. The experiment results show improvements in performance as compared to other state-of-the-art methods like DL-Q, End to End DNN and SVM. With various assessments and experimentation, it is observed that the proposed SMO-based performs better in terms of accuracy of 98.92%, precision of 98.12%, recall of 98.9%, and F1-score 95.90%, respectively for the WISDM dataset. There is an improvement in performances for other datasets. Also, the Error rate has reduced to 2.8% as compared to other state-of-the-art methods.