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Adaptive Step Detection Algorithm for Wireless Smart Step Counter

Authors: UkJae Ryu; Kyungho Ahn; Entae Kim; Munhae Kim; Boyeon Kim; Sunghun Woo; Yunseok Chang;

Adaptive Step Detection Algorithm for Wireless Smart Step Counter

Abstract

we had designed and implemented a smart step counter with adaptive step detection algorithm that can enhance the accuracy of step detection using step amplitude. This algorithm can pick step count out over 99% of accuracy through the adaptive step detection algorithm. To prove the effectiveness of the new algorithm, we made a noise filtering process with the amplitude threshold from the amplitude of energy value that found out the detection error was increasing, and it''s the key idea of the variable amplitude threshold that can be adapted on the continuous data evaluation. The experiment results shows that the adaptive step detection algorithm can catch the average step count accuracy up to 98.9% at 10 Hz sampling rate and 99.6% at 20Hz sampling rate.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Top 10%
Top 10%
Top 10%
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