Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

The recent development of machines exhibiting intelligent characteristics involves numerous techniques including computer hardware and software architecture development. Many different hardware devices, wearable sensors, machine learning, and deep learning model implementations are being applied in human activity recognition (HAR) applications in recent times. However, to develop high accuracy classification systems for activity recognition using ilow-cost hardware technology is of significant importance. To achieve this goal this study uses sensor data from two low-cost sensors, gyroscope and accelerometer along with the implementation of an Artificial Neural Network (ANN) based deep learning model for HAR. In particular, Deep Stacked Multilayered Perceptron (DS-MLP) has been proposed. In the implementation of DS-MLP, an ANN model has been used as a meta-learner while five MLP models have been used as base-learners. In this study, these base-learners and meta-learner have been combined using a stack ensemble technique. The performance evaluations have been done first on the applicability of individual base-models followed by the application of DS-MLP, the results prove the high accuracy of 97.3% and 99.4% for heterogeneous datasets used for testing. The performance of the proposed DS-MLP models has been compared to some existing machine learning classifiers and several state-of-the-art activity recognition systems. The comparative result analysis also proves that the proposed system performed better than these classification approaches in terms of important performance metrics such as accuracy, precision, recall, F-score, Cohen's Kappa, and Mathew correlation coefficient.