Using smartphone-based accelerometer to detect travel by metro train

Abstract

We look at the problem of using accelerometer in smartphones to detect whether the user is at a metro train station or in a metro train. Currently, we have solutions to detect simple activities, such as sitting or walking. Our work for this thesis investigates the more complex problem of discerning \in-train" from \in-metro-station" activities which internally are composed of several simple activities. We perform the task of distinguishing the \in-train" from \in-metrostation" patterns using classic classification techniques with two different data representations namely, statistical features and ECDF-based features. Another major contribution through this thesis is to solve the challenge of the considerable class imbalance with majority of samples belonging to the \in-train" patterns by improvising existing classification algorithms to counter the effect of class imbalance. Our fndings are useful for any other problem of using sensor data to classify activities. We evaluated our solution using about 23 hours of data collected using six different models of smartphones from over seven different metro/subway stations situated in New Delhi, India. Our detection accuracy is over 98%.