Smartphone-Based Unconstrained Step Detection Fusing a Variable Sliding Window and an Adaptive Threshold

Abstract

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding window and an adaptive threshold is proposed. In this method, the dynamic updating algorithm of a peak threshold is developed, and the minimum peak value filtered after a sliding window filter is used as the adaptive peak threshold, which solves the problem that the peak threshold of different motion states is difficult to update adaptively. Then, a variable sliding window collaborative time threshold method is proposed, which solves the problem that the adjacent windows cannot be contacted, and the initial peak and the end peak are difficult to accurately identify. To evaluate the performance of the proposed unconstrained step detection algorithm, 50 experiments in constrained and unconstrained states are conducted by 25 volunteers holding 21 different types of smartphones. Experimental results show: The average step counting accuracy of the proposed unconstrained step detection algorithm is over 98%. Compared with the open source program Stepcount, the average step counting accuracy of the proposed algorithm is improved by 10.0%. The smartphone-based unconstrained step detection fusing a variable sliding window and an adaptive threshold has a strong ability to adapt to complex unconstrained states, and the average step counting accuracy rate is only 0.6% lower than that of constrained states. This algorithm has a wide audience and is friendly for different genders and smartphones with different prices.

A real-time step detection algorithm based on dynamic peak-valley change threshold screening and dual-time threshold constraints

“…Using fixed peak value and time threshold constraint will not satisfy the step counting accuracy under various motion states. Fast Fourier transform (FFT) can convert time domain signals into frequency domain signals 15 , so this paper gathered 5 volunteers to test and analyze step frequency under different motion states based on FFT algorithm. After the test of 5 volunteers, we found that the step frequency of walking at normal walking was 1.4-1.8Hz, corresponding to about 0.55-0.71s.…”

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

“…Integration of part into T_set tuple (19) else if (20) Integration of intersect (part, IP) into I_set tuple (21) end if (22) end for (23) are mainly used in the study as the main factors to measure the temporal characteristics. However, the window size and input threshold set in the sliding window algorithm also have an impact on the fnal compression efect [36,37].…”

Strong Tracking Particle Filter Based on the Chi-Square Test for Indoor燩ositioning

“…Therefore, some scholars combine wireless indoor positioning algorithms with pedestrian dead reckoning (PDR) algorithms to achieve continuous and accurate indoor positioning [7][8]10,21]. The PDR algorithm can only rely on the IMU sensor embedded in the smartphone to estimate the direction and distance of the movement of the moving target and calculate the location of the moving target [7,22]. Accordingly, PDR cannot be affected by NLOS.…”