Human context recognition with reduced feature space vectors for resource constraint gadgets

Abstract

The aim of this paper is to determine the minimal features to train classification algorithms pertaining to context recognition using data collected from accelerometer sensor in smartphones. A detailed experimental evaluation of various time and frequency domain features on raw accelerometer sensor data collected from smartphones leads to the most influential minimal collection that aid in recognizing human context in resource constraint gadgets. To substantiate the study, four classifiers, namely, Logistic Regression, Support Vector Machine, Artificial Neural Network and Long Short Term Memory Recurrent Neural Networks are trained on six activities - Sitting, Standing and lying (sedentary activities), Walking, Walking Upstairs and Walking Downstairs (dynamic activities). The raw accelerometer values from UCI public dataset and the data collected from Android application is used to build classification model. Classifier performance on both datasets showed 90% accuracy with four features taken over each axes of 3-axis accelerometer.