Step-Detection and Adaptive Step-Length Estimation for Pedestrian Dead-Reckoning at Various Walking Speeds Using a Smartphone.
We propose a walking distance estimation method based on an adaptive step-length estimator at various walking speeds using a smartphone. First, we apply a fast Fourier transform (FFT)-based smoother on the acceleration data collected by the smartphone to remove the interference signals. Then, we analyze these data using a set of step-detection rules in order to detect walking steps. Using an adaptive estimator, which is based on a model of average step speed, we accurately obtain the walking step length. To evaluate the accuracy of the proposed method, we examine the distance estimation for four different distances and three speed levels. The experimental results show that the proposed method significantly outperforms conventional estimation methods in terms of accuracy.
School of Electrical Engineering, Kookmin University, 861-1 Jeongnung-dong, Seongbuk-gu, Seoul 136-702, Korea. ngochuynh@kookmin.ac.kr.
Ngoc-Huynh
NH
Ho
School of Electrical Engineering, Kookmin University, 861-1 Jeongnung-dong, Seongbuk-gu, Seoul 136-702, Korea. phtruong@kookmin.ac.kr.
Phuc Huu
PH
Truong
School of Electrical Engineering, Kookmin University, 861-1 Jeongnung-dong, Seongbuk-gu, Seoul 136-702, Korea. gm1004@kookmin.ac.kr.
Gu-Min
GM
Jeong
D000054:Acceleration; D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008875:Middle Aged; D000069636:Pedestrians; D000068997:Smartphone; D016138:Walking; D000072797:Walking Speed / Q000502:physiology*; D055815:Young Adult
FFT filter; PDR; Weinberg method; step length; step-detection