UPTIME: Ubiquitous pedestrian tracking using mobile phones

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Abstract
The mission of tracking a pedestrian is valuable for many applications including walking distance estimation for the purpose of pervasive healthcare, museum and shopping mall guides, and locating emergency responders. In this paper, we show how accurate and ubiquitous tracking of a pedestrian can be performed using only the inertial sensors embedded in his/her mobile phone. Our work depends on performing dead reckoning to track the user's movement. The main challenge that needs to be addressed is handling the noise of the low cost low quality inertial sensors in cell phones. Our proposed system combines two novel contributions: a novel step count estimation technique and a gait-based accurate variable step size detection algorithm. The step count estimation technique is based on a lightweight finite state machine approach that leverages orientation-independent features. In order to capture the varying stride length of the user, based on his changing gait, we employ a multi-class hierarchical Support Vector Machine classifier. Combining the estimated number of steps with the an accurate estimate of the individual stride length, we achieve ubiquitous and accurate tracking of a person in indoor environments. We implement our system on different Android-based phones and compare it to the state-of-the-art techniques in indoor and outdoor testbeds with arbitrary phone orientation. Our results in two different testbeds show that we can provide an accurate step count estimation with an error of 5.72%. In addition, our gait type classifier has an accuracy of 97.74%. This leads to a combined tracking error of 6.9% while depending only on the inertial sensors and turning off the GPS sensor completely. This highlights the ability of the system to provide ubiquitous, accurate, and energy efficient tracking.

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