Abstract
Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01).
Highlights
The Global Positioning System (GPS) is unreliable for indoor localization applications
Our proposed method combined peak detection with suitable minimal peak distance, minimal peak prominence, dynamic thresholding (the average of maximal and minimal values of Acc(j) data in a window size), and vibration elimination (using Root Mean Square (RMS) to compute the amplitude of acceleration to eliminate all peaks that fluctuated around gravity acceleration (g = 9.81 m/s2 )) in step counting
The process of recording data was gathered from the experiment that was executed on eight male volunteers with the age: 18–28, height: 1.65–1.78 m, and weight: 58–76 kg who were selected from The University of Fire Fighting and Prevention (UFFP)
Summary
The Global Positioning System (GPS) is unreliable for indoor localization applications. An indoor positioning system is essential and attractive for both researchers and companies since it is employed in widespread practical applications; several techniques have been proposed for it. Indoor positioning is based on pre-installed sensors/devices with a high accuracy of such as camera, wireless sensor network, wireless network, UWB (Ultra-wideband), and Doppler radar [1,2,3,4,5], but limitations of these techniques are that it is expensive and only applicable on pre-installed environments.
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