PERFORMANCE ANALYSIS OF MLP AND SVM BASED CLASSIFIERS FOR HUMAN ACTIVITY RECOGNITION USING SMARTPHONE SENSORS DATA

Abstract

Days have gone when Mobile Phone used to be matter luxury, it has become a significant need for rapidly evolving fast track world. Intelligent aspects of the computing device enhance the importance of the interface development since; efficient collaboration always relies on the good communication between man and machine. The information about mobile device and user activity, environment, other devices, location and time can be utilized in different situations to enhance the interaction between the user and the device. Here, we presented our work in which certain types of human physical activities using accelerometer and gyroscope data generated by a mobile device. The benchmark Human Activity Recognition dataset is considered for this work is acquired from UCI Machine Learning Repository which is available in public domain. MLP and SVM Classifiers were tested using various time domain and frequency domain features. We found the using Multi Layer Perceptron with Processing Element 6, Learning rate 0.05, Momentum 0.1 reach an overall accuracy of 98.11%. Keywords— Context; Framework; Adaptive User Interfaces; Classifier, Multilayer Perceptron, Support Vector