A Statistical and Deep Learning Approach for Human Activity Recognition

Evolution of sensor-based data accumulation process regulated the fact-finding stratum concerning the scrutiny of human-centered motion towards an exemplary appearance. Such evolution enriched the fabrication of a variety of functional datasets, which facilitates the exploration of facts of various research domain. The MHEALTH is such a secondary dataset, that was prepared so as to facilitate the exploration regarding Human Activity Recognition (HAR). This paper performs a comparative study on human activity recognition process in terms of employment of two different data preprocessing methods accompanied by five fashionable classifiers entitled as Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Multilayer Perceptron (MLP) and Deep Convolutional Neural Network (CNN). We employed the MHEALTH dataset to realize the human activity recognition process. The dataset encompasses information regarding 12 human activities subjecting 10 volunteers. Sensors were employed for the data accumulation process namely Accelerometer, Gyroscope and Magnetometer. Data preprocessing methods, that were employed on the mentioned dataset are elimination of null label instances and uniformity of unbalanced classes. The goal of this study is to analyze performance of different classifiers in terms of the mentioned data preprocessing methods and also identifying the process for which the classifiers exhibit superior accuracy.