Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005 · 218 citations · 9 references
ComputingComputer EngineeringMobile ComputingMovement AnalysisSensorsDevice AuthenticationGait AnalysisMobile SensingMotion CaptureMotion SensorsWearable Health TechnologiesAccelerometersAccelerometryEqual Error RateError Rate AnalysisGait Recognition
Protecting portable devices is becoming more important, not only because of the value of the devices themselves, but for the value of the data in them and their capability for transactions, including m‑commerce and m‑banking. The study presents an unobtrusive, natural method for identifying the carrier of portable devices. The method extracts steps from device acceleration data, normalizes and averages them, aligns with a stored template, and computes cross‑correlation to quantify similarity. Experiments with 36 subjects achieved an equal error rate of 6.4%.
Protecting portable devices is becoming more important, not only because of the value of the devices themselves, but for the value of the data in them and their capability for transactions, including m-commerce and m-banking. An unobtrusive and natural method for identifying the carrier of portable devices is presented. The method uses acceleration signals produced by sensors embedded in the portable device. When the user carries the device, the acceleration signal is compared with the stored template signal. The method consists of finding individual steps, normalizing and averaging them, aligning them with the template and computing cross-correlation, which is used as a measure of similarity. Equal Error Rate of 6.4% is achieved in tentative experiments with 36 test subjects.
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Analyzing and recognizing walking figures in XYT
Automatic gait recognition based on statistical shape analysis
Liang Wang, Tieniu Tan, Weiming Hu et al. · IEEE Transactions on Image Processing · 2003 · 333 citations