The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
A Multi-view Method for Gait Recognition Using Static Body Parameters
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
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
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
A Floor Sensor System for Gait Recognition
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
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
Using height sensors for biometric identification in multi-resident homes
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
An Analysis of Different Approaches to Gait Recognition Using Cell Phone Based Accelerometers
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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Ground reaction forces generated during normal walking have recently been used to identify and/or classify individuals based upon the pattern of the forces observed over time. One feature that can be extracted from vertical ground reaction forces is body mass. This single feature has identifying power comparable to other studies that use multiple and more complex features. This study contributes to understanding the role of body mass in identification by (1) quantifying the accuracy and precision with which body mass can be obtained using vertical ground reaction forces, (2) quantifying the distribution of body mass across a population larger than has previously been studied in relation to gait analysis, and (3) quantifying the expected identification capabilities of systems using body mass as a weak biometric. Our results show that body mass can be measured in a fraction of a second with less than a 1 kilogram standard deviation of error.