DePedo: anti periodic negative-step movement pedometer with deep convolutional neural networks

Shao W., Luo H., Zhao F., Wang C., Crivello A., Tunio M. Z.

Legged locomotion  Training  Neural networks  Detectors  Feature extraction  Acceleration  Foot 

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.

Source: ICC 2018 - IEEE International Conference on Communications, Kansas City, MO, USA, USA, 20-24 May 2018

Publisher: IEEE Communications Society, Piscataway, USA

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