A Novel Accelerometer-Based Technique for Robust Detection of Walking Direction

Shaikh, Muhammad Faraz; Salčić, Zoran; Wang, Kevin I‐Kai

doi:10.1109/tbme.2017.2774924

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…(2017), for example, a method for detecting half‐turn stairs based on the study of the characteristics specific to this movement is proposed. In Shaikh, Salcic, and Wang (2018), a method of estimating the walking direction using dominant trend duration allows us to integrate a detection of backward movements. Indeed, the direction of motion is not sufficient, it is also necessary to know the sense of motion.…”

Section: State Of the Artmentioning

confidence: 99%

HEAD: smootH Estimation of wAlking Direction with a handheld device embedding inertial, GNSS, and magnetometer sensors

Perul¹,

Renaudin²

2020

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Pedestrian navigation with handheld sensors is still particularly complex. Pedestrian Dead Reckoning method is generally used, but the estimation of the walking direction remains problematic because the device's pointing direction does not always correspond to the walking direction. To overcome this difficulty, it is possible to use gait modeling based approaches. But, these methods suffer from sporadic erroneous estimates and their accumulation over time. The HEAD (smootH Estimation of wAlking Direction) filter uses WAISS and MAGYQ angular estimates as observations to correct the walking direction and to obtain more robust and smooth results. TDCP updates are applied to constrain the walking direction estimation error while pseudo‐ranges directly update the position. HEAD is tested by 5 subjects over 21 indoor/outdoor acquisitions (between 720 m and 1.3 km). A 54% improvement is achieved thanks to the fusion in texting mode. The median obtained angular error is 5.5∘ in texting mode and 12∘ in pocket mode.

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Section: State Of the Artmentioning

confidence: 99%

HEAD: smootH Estimation of wAlking Direction with a handheld device embedding inertial, GNSS, and magnetometer sensors

Perul¹,

Renaudin²

2020

Navigation

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Smartwatch-Based Kinematic Walking Direction Estimation Using Paired Principal Component Analysis

Park,

Lee,

Park

et al. 2024

IEEE Access

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The dynamic behavior of pedestrians causes a misalignment problem between the sensor orientation and the walking direction, which hinders the performance of pedestrian dead reckoning (PDR) systems. Pedestrians wearing smartwatches are constantly faced with this problem when running. In this paper, we propose a novel kinematic modeling of arm swing that segments arm swing motion from the movement of the center of mass of the body. The proposed decomposition method allows for effective negation of the sensor outputs due to the redundant motion that obstructs the estimation of the true walking direction. The correct direction vector is computed by deducting the direction vector of the arm swing from that of the entire motion, which are both derived from performing two separate principal component analyses (PCA). The performance of the proposed method was evaluated through several experiments. In the running track experiment, the proposed method demonstrates the best performance, with 57% -70% performance improvement compared to the existing methods. In the general scenario involving both walking and running, the proposed method outperforms the baseline method by 56%, improving the generality of the PCA-based methods.INDEX TERMS Gait kinematics, inertial sensors, pedestrian dead reckoning (PDR), principal component analysis (PCA), smartwatch, walking direction estimation.

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An Improved PDR/UWB Integrated System for Indoor Navigation Applications

Guo¹,

Zhang²,

Gui³

et al. 2020

IEEE Sensors J.

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A Novel Accelerometer-Based Technique for Robust Detection of Walking Direction

Abstract: The approach can be employed in different contexts, such as enabling pedestrian localization approaches to accommodate back stepping or any application that requires knowledge of changing directions while walking.

Cited by 6 publications

References 26 publications

HEAD: smootH Estimation of wAlking Direction with a handheld device embedding inertial, GNSS, and magnetometer sensors

HEAD: smootH Estimation of wAlking Direction with a handheld device embedding inertial, GNSS, and magnetometer sensors

Smartwatch-Based Kinematic Walking Direction Estimation Using Paired Principal Component Analysis

An Improved PDR/UWB Integrated System for Indoor Navigation Applications

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