Ph.D. Theses

Context Modeling with Inertia Mobile Sensor

By Yu Chen
Advisor: Peter Fox
May 18, 2015

Mobile sensors have been around for decades and the number of different kinds is increasing rapidly. With the ubiquitous sensors in public facilities, home surveillance equipments and personal mobile devices, there is a great opportunity to leverage those sensors to expand the horizon of human’s sensitivity to understand the surroundings as well as each individual in a better way. However, mining the time series data produced by those sensors requires lots of domain knowledge and skills in signal processing, data mining and machine learning techniques, which are not everyone’s expertise. Therefore, in order to make full use of the sensor data and understand the environment and human, it is vital to have a system which is efficient, scalable and reusable, that is capable of analyzing and gaining knowledge from time series data produced by various kinds of sensors.

In this dissertation, the first part of the work focuses on developing motif detection algorithm to extract time series data patterns efficiently in a scalable approach. The second part of the work is to demonstrate the practicability of the algorithm along with the time series data analysis system in real application of understanding different perspectives of human activity via inertial sensors on mobile devices. A real time human physical activity recognition web service is developed in understanding sensor data produced by mobile phones. The capability of the system has also been demonstrated via a hacking system that is able to detect and recover user’s virtual keyboard input on mobile phone by sampling and analyzing data from background running accelerometer and gyroscope without direct access to user’s touch screen. The system has also been further evaluated under a more constructive application. ”UbiKeyboard” has been developed to detect and predict user’s intentional input by analyzing patterns in time series data generated by a wearable smart-glove that is equipped with accelerometer and gyroscope. With the help of a web scale natural language model, the system is able to recognize user’s intentional input with even higher accuracy.

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