Autocorrelation analysis of accelerometer signal to detect and count steps of smartphone users

Santos, João Paulo; Costa, António; Nicolau, Maria João

doi:10.1109/ipin.2019.8911755

Cited by 8 publications

(8 citation statements)

References 7 publications

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“…Recent literature has proposed using the camera [146] and gyroscope data [147] for step counting, and we focus here on step counting with a built-in accelerometer for smartphones. Time domain approaches: Time-domain methods can be divided into thresholding [148], peak detection [149], zero-crossing [150], and auto-correlation [151][152][153].…”

Section: Step Detectionmentioning

confidence: 99%

“…Second, the windowed peak detection algorithm is overall the best choice for step counting, regardless of the smartphone placement. Santos et al [153] first determined the peak frequency by subtracting its average value from the acceleration signal and using Fast Fourier transform. A band-pass filter is then used to remove high frequencies and frequencies below 1 Hz.…”

Section: Step Detectionmentioning

confidence: 99%

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A Survey of Magnetic-Field-Based Indoor Localization

Ouyang

Abed-Meraim

2022

Electronics

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Magnetic fields have attracted considerable attention in indoor localization due to their ubiquitous and infrastructure-free characteristics. This survey provides a comprehensive review of magnetic-field-based indoor localization methods. We first introduce characteristics of the magnetic field, its advantages, and its challenges. We then describe the magnetometer model and the effect of ferromagnetic interference. We also present coordinate systems commonly used for magnetic field localization and describe their transformation relationships. We then compare the existing publicly available magnetic field benchmark datasets, present magnetometer calibration algorithms, and show how efficiently magnetic field maps can be built. We also summarize state-of-the-art magnetic field localization methods (e.g., magnetic landmarks, dynamic time warping, magnetic fingerprinting, filters, simultaneous localization and mapping, and neural network). The smartphone-based pedestrian dead reckoning approach is also reviewed.

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Section: Step Detectionmentioning

confidence: 99%

Section: Step Detectionmentioning

confidence: 99%

A Survey of Magnetic-Field-Based Indoor Localization

Ouyang

Abed-Meraim

2022

Electronics

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“…The proposed algorithms' average accuracy ranged from 95% to 97%, considering different walking speeds. Further, the step counting based on the smartphone's accelerometer has been developed in Indoor Positioning System as proposed in [5]. The approach relies on pattern recognition for step detection and counting.…”

Section: Advances In Science Technology and Engineering Systems Journalmentioning

confidence: 99%

“…Avg. Accuracy (Running) Proposed Algorithm 99.06% 96.87% [4] 96.5% N.A [6] 93% N.A [12] 96.6% N.A [5] 94% 78.3% [3] 93.7% N.A…”

Section: Avg Accuracy (Normal Walk)mentioning

confidence: 99%

“…One of the aspects has been considered in the literature: smartphone placement's impact on measurement accuracy. The smartphone's position and placement might cause jitter during the human motion, which leads to counting false steps [5]. Also, remaining still, watching videos, playing games, and many other activities can lead to a wrong step count [6].…”

Section: Introductionmentioning

confidence: 99%

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Algorithm Design for Accurate Steps Counting Based on Smartphone Sensors for Indoor Applications

Muhsen¹,

Al-Amaydeh²,

Al-Hamlan³

2020

Adv. sci. technol. eng. syst. j.

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This paper aims to design an accurate and robust algorithm for counting the steps based on the smartphone's accelerometer for indoor applications. Different daily activities have been considered in the experimental scenarios, including normal walking with various smartphone positions and running activities. The detection process's accuracy is ensured by setting an appropriate segregation approach and defining a fixed threshold for each experiment. The algorithm generates a new envelope signal that mimics the collected steps signal without the vibrations and noise elements to avoid miscounting the exact steps. The followed approach results have been compared with the pedometer applications on the android platform and HUAWEI Watch GT-731. The proposed algorithm showed improved detection accuracy in contrast to the other literature approaches. The obtained accuracy was around 99% for the standard walking scenario and approximately 97% for the running activity scenario.

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