Person Identification Using Automatic Height and Stride Estimation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
Soft biometrics-combining body weight and fat measurements with fingerprint biometrics
Pattern Recognition Letters
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Group-specific face verification using soft biometrics
Journal of Visual Languages and Computing
Gait recognition using wearable motion recording sensors
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Design considerations for the WISDM smart phone-based sensor mining architecture
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Accelerometer-based on-body sensor localization for health and medical monitoring applications
Pervasive and Mobile Computing
Hi-index | 0.00 |
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).