A Proposed ConvXGBoost Model for Human Activity Recognition with Multi Optimizers

The wide use of smartphones and later smartwatches equipped with a set of sensors such as location, motion, and direction blaze the trail for researchers to better recognize human activity. However, researches on using inertial or motion sensors (i.e., accelerometer, gyroscope) for human activity recognition (HAR) has intensified and reside a great confrontation to be faced. Lately, many deep learning methods have been suggested to improve the human activity classification and discrimination performance to reach an optimal accuracy. Therefore, this paper applies a Convolutional eXtreme Gradient Boosting (ConvXGBoost), which combines Convolutional Neural Network (CNN) represented by AlexNet to learn the input features automatically, followed by XGBoost decision tree used to predict the class label and thereof recognize the performed activity. Human activities are collected from sensors as time series data. Therefore, we suggested using one-dimensional AlexNet (1D AlexNet) model instead of 2D. The AlexNet model is compiled with two optimizers Adam and Stochastic Gradient Descent (SGD) which are applied consecutively. The suggested architecture was trained and evaluated on the “WISDM Smartphone and Smartwatch Activity and Biometric Dataset” that consists of raw data for eighteen activities recorded from phone and watch. The experiments revealed that using multi optimizer with a convolutional neural network improved the accuracy of recognition by 5%. Moreover, a proposed ConvXGBoost model outperformed the performance of other models works with the dataset as mentioned above with an overall accuracy of 98-99% depends on the device used.