Abstract:
Pedometer is an enabling technique for smartphone- based pedestrian positioning systems. Because the sensor drifts, these algorithms can only estimate moving distances fr...Show MoreMetadata
Abstract:
Pedometer is an enabling technique for smartphone- based pedestrian positioning systems. Because the sensor drifts, these algorithms can only estimate moving distances from step counts. In order to detect step events, researchers have tried to leverage the peak detection and the periodicity attribute of step acceleration signals. However, many human behaviors are having acceleration peaks and periodic, causing traditional detectors error- prone when the phone is shaken periodically leading state-of-the-art system to high false positive ratio and consequently to big mistake of distance estimations. Based on the acceleration feature analysis of step events, we present a deep convolution neural network based step detection scheme to improve the pedometer robustness. Finally, the proposed step detection algorithm is tested in a realistic situation, showing a high anti periodic negative-step movement capability.
Date of Conference: 20-24 May 2018
Date Added to IEEE Xplore: 30 July 2018
ISBN Information:
Electronic ISSN: 1938-1883