A Robust Step Detection and Stride Length Estimation for Pedestrian Dead Reckoning Using a Smartphone
| Autor: | Yingbiao Yao, Pan Lei, Xin Xu, Wei Fen, Xiaorong Xu, Xuesong Liang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Dynamic time warping
Computer science business.industry 010401 analytical chemistry Feature extraction Pattern recognition Pedestrian Stride length 01 natural sciences 0104 chemical sciences Acceleration Dead reckoning Step detection Artificial intelligence Electrical and Electronic Engineering business Instrumentation |
| Zdroj: | IEEE Sensors Journal. 20:9685-9697 |
| ISSN: | 2379-9153 1530-437X |
| Popis: | As an infrastructure-free positioning and navigation method, pedestrian dead reckoning (PDR) is still a research hotspot in the field of indoor localization. Step detection (SD) and stride length estimation (SLE) are two key components of PDR, and it is a challenging problem to apply SD and SLE to different walking patterns. Focusing on this problem, this paper proposes a robust SD and SLE method based on recognizing three walking patterns (i.e., Normal Walk, March in Place, and Quick Walk) using a smartphone. First, we propose a dynamic time warping–based peak prediction with zero-crossing detection to improve the SD accuracy. In particular, the proposed SD can accurately identify the starting and ending points of each step in the three walking patterns. Second, according to the extracted features of each step, a random forest algorithm with classification proofreading is used to recognize the three walking patterns. Finally, an improved SLE model is proposed for the different walking patterns to achieve a higher SLE accuracy. The experimental results show that, on average, the SD accuracy is about 97.9%, the recognition accuracy is about 98.4%, and the relative error of the estimated walking distance is about 3.0%, which outperforms those of the existing commonly used SD and SLE methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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