Recent Scholarship
Design considerations for the WISDM smart phone-based sensor mining architecture
Smart phones comprise a large and rapidly growing market. These devices provide unprecedented opportunities for sensor mining since they include a large variety of sensors, including an: acceleration sensor (accelerometer), location sensor (GPS), direction sensor (compass), audio sensor (microphone), image sensor (camera), proximity sensor, light sensor, and temperature sensor. Combined with the ubiquity and portability of these devices, these sensors provide us with an unprecedented view into people’s lives—and an excellent opportunity for data mining. But there are obstacles to sensor mining applications, due to the severe resource limitations (e.g., power, memory, bandwidth) faced by mobile devices. In this paper we discuss these limitations, their impact, and propose a solution based on our WISDM (WIireless Sensor Data Mining) smart phone-based sensor mining architecture.
Jeffrey W. Lockhart, Gary M. Weiss, Jack C. Xue, Shaun T. Gallagher, Andrew B. Grosner, and Tony T. Pulickal, “Design Considerations for the WISDM Smart Phone-Based Sensor Mining Architecture,” Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, San Diego, CA (at KDD-2011): 25-33.
Identifying user traits by mining smart phone accelerometer data
Abstract: Smart phones are quite sophisticated and increasingly incorporate diverse and powerful sensors. One such sensor is the tri-axial accelerometer, which measures acceleration in all three spatial dimensions. The accelerometer was initially included for screen rotation and advanced game play, but can support other applications. In prior work we showed how the accelerometer could be used to identify and/or authenticate a smart phone user [11]. In this paper we extend that prior work to identify user traits such as sex, height, and weight, by building predictive models from labeled accelerometer data using supervised learning methods. The identification of such traits is often referred to as “soft biometrics” because these traits are not sufficiently distinctive or invariant to uniquely identify an individual—but they can be used in conjunction with other information for identification purposes. While our work can be used for biometric identification, our primary goal is to learn as much as possible about the smart phone user. This mined knowledge can be then be used for a number of purposes, such as marketing or making an application more intelligent (e.g., a fitness app could consider a user’s weight when calculating calories burned).
Gary M. Weiss and Jeffrey W. Lockhart, “Identifying User Traits by Mining Smart Phone Accelerometer Data,” Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data San Diego, CA. (at KDD-2011): 61-69.