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Noise segmentation for step detection and distance estimation using smartphone sensor data

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Abstract

As for the low-cost and accuracy benefits of integrating Artificial Intelligence (AI) technologies in smartphones, the embedded smart sensors have been utilized in indoor localization and distance estimation applications rather than the requirement of wearable sensors. However, the effect of a potential noise during the user’s movement is still an issue, i.e. irrelevant movements. Knowing that the noise can severely impair the accuracy of distance estimation by increasing or decreasing the number of estimated steps. Therefore, this paper proposes a distance estimation method based on dynamic step size and shape. It aims at removing the noise from the bit signal and detecting the number of steps regardless of the noise using the magnitude value for the three-axis accelerometer data. Furthermore, it involves the variables of the user's height with relation to the step size to estimate the walking distance. The experiment sets allow four phases including data collection and filtering, peak detection, dynamic step size detection, and distance estimation. The low-pass filter is applied to remove the resultant noise of irrelevant movements such as handshaking, texting, swinging, etc. The experimental results showed that the proposed method of step detection, step size, and walking distance estimations achieved significant accuracy improvement over existing current approaches reaches (98%) when a pedestrian was walking in multiple mode changes.

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Authors and Affiliations

  1. CS Department, Mutah University, Mutah, Karak, Jordan

    Ahmad Abadleh, Bassam M Al-Mahadeen, Rami M AlNaimat & Omar Lasassmeh

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  1. Ahmad Abadleh
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  2. Bassam M Al-Mahadeen
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  3. Rami M AlNaimat
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  4. Omar Lasassmeh
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Correspondence to Omar Lasassmeh.

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Abadleh, A., Al-Mahadeen, B., AlNaimat, R. et al. Noise segmentation for step detection and distance estimation using smartphone sensor data. Wireless Netw 27, 2337–2346 (2021). https://doi.org/10.1007/s11276-021-02588-0

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