Zero Velocity Potential Update (ZUPT) as a Correction Technique

Suresh, Rahul; Sridhar, Vinay; J, Pramod; Talasila, Viswanath

doi:10.1109/iot-siu.2018.8519902

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…The result of this classification was then used to adjust the window length w f + w b + 1 (see Section II-B) in the zero-velocity detection. Likewise, instead of adapting certain detection parameters, a conceptually similar idea is to include estimated navigation quantities, such as speed or Euler angles, in the computation of the test statistic (likelihoodratio) [53], [65], [78]. Finally, instead of using gait cycle segmentation as in Section V-B, gait cycle models can also be incorporated into zero-velocity detectors by clustering the stance phases based on their time length.…”

Section: Other Robust Detectorsmentioning

confidence: 99%

Fifteen Years of Progress at Zero Velocity: A Review

Wahlström

Skog

2021

IEEE Sensors J.

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Fifteen years have passed since the publication of Foxlin's seminal paper "Pedestrian tracking with shoe-mounted inertial sensors". In addition to popularizing the zero-velocity update, Foxlin also hinted that the optimal parameter tuning of the zero-velocity detector is dependent on, for example, the user's gait speed. As demonstrated by the recent influx of related studies, the question of how to properly design a robust zerovelocity detector is still an open research question. In this review, we first recount the history of foot-mounted inertial navigation and characterize the main sources of error, thereby motivating the need for a robust solution. Following this, we systematically analyze current approaches to robust zero-velocity detection, while categorizing public code and data. The article concludes with a discussion on commercialization along with guidance for future research.

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Section: Other Robust Detectorsmentioning

confidence: 99%

Fifteen Years of Progress at Zero Velocity: A Review

Wahlström

Skog

2021

IEEE Sensors J.

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“…Given the system described in eqs. (10) and (11), the a-priori state estimate xk|k−1 can be calculated by…”

Section: Kalman Filtermentioning

confidence: 99%

“…This is due to the fact that the feet as the only body segment have zero velocity phases during a gait cycle, which enables a correction of the integration drift that usually result from a numerical integration of noise-affected quantities. The correction phase is commonly known as the Zero Velocity Update (ZUPT) and was extensively investigated in several approaches for indoor pedestrian navigation and mobile gait analysis [8], [9], [10], [11], [12]. Sabatini et al proved the applicability of a foot-mounted IMU and ZUPT for walking speed estimation during treadmill trials with healthy adult subjects yielding an a root mean square error (RMSE) of 0.18 km h 1 at walking speeds between 3 6 km h 1 .…”

Section: Introductionmentioning

confidence: 99%

Model-Based Step Length Estimation Using a Pendant-Integrated Mobility Sensor

Lueken¹,

Loeser²,

Weber³

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…In an INS, the IMU is usually mounted on the pedestrian's foot. By calculating the sensor data at each moment and compensating for sensor errors using the zero velocity update (ZUPT) algorithm [11,12], the extended Kalman filter (EKF) is used to determine the pedestrian's position and track their movements [13]. INS can provide more accurate posture and position estimates than PDR, making it more frequently used in practical applications.…”

Section: Introductionmentioning

confidence: 99%

An adaptive threshold method with error correction for pedestrian inertial navigation system

Yang

Yong

Liang

et al. 2023

Meas. Sci. Technol.

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As one of the effective methods to reduce the accumulated error of pedestrian navigation system based on foot-mounted inertial measurement unit (IMU), zero velocity update (ZUPT) has been widely used in the global navigation satellite system (GNSS) signal rejection environments, which requires accurate zero velocity detection. However, the detection accuracy of the classical fixed threshold method is not satisfactory under complex gaits. In this paper, an adaptive threshold method with error correction is proposed to improve the detection accuracy without adding sensors. In this method, an adaptive threshold model based on gait intensity is established, which can dynamically adjust the zero velocity detection threshold under complex gaits. Afterwards, error correction is proposed to identify and correct misjudgments in zero velocity detection by analyzing the zero velocity interval in one gait cycle. We build an actual pedestrian inertial navigation system to evaluate the proposed method. The experimental results show that the average zero velocity detection accuracy of the proposed method under complex gaits of different people is 98.67%, and the end-to-end positioning error is about 0.53% of the total distance during long-distance movement.

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Zero Velocity Potential Update (ZUPT) as a Correction Technique

Cited by 15 publications

References 5 publications

Fifteen Years of Progress at Zero Velocity: A Review

Fifteen Years of Progress at Zero Velocity: A Review

Model-Based Step Length Estimation Using a Pendant-Integrated Mobility Sensor

An adaptive threshold method with error correction for pedestrian inertial navigation system

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