A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications

Zihajehzadeh, Shaghayegh; Loh, Darrell; Lee, Tien Jung; Hoskinson, Reynald; Park, Edward J.

doi:10.1016/j.measurement.2015.05.023

Cited by 61 publications

(34 citation statements)

References 31 publications

(52 reference statements)

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“…Considering that the acceleration data from tri-axial accelerometer in a wearable inertial sensor can be integrated to get the velocity, integration-based approaches have been widely used for speed tracking [ 30 ]. The main challenge in integration-based approaches is the velocity drift over time that happens as a result of time-varying bias in MEMS-based inertial sensors [ 31 ]. To mitigate the drift, some researchers have proposed the detection of periodic foot stance phases during walking to reset the velocity to zero through a process called zero velocity update (ZUPT) [ 30 – 34 ].…”

Section: Introductionmentioning

confidence: 99%

Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor

Zihajehzadeh

Park

2016

PLoS ONE

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Walking speed is widely used to study human health status. Wearable inertial measurement units (IMU) are promising tools for the ambulatory measurement of walking speed. Among wearable inertial sensors, the ones worn on the wrist, such as a watch or band, have relatively higher potential to be easily incorporated into daily lifestyle. Using the arm swing motion in walking, this paper proposes a regression model-based method for longitudinal walking speed estimation using a wrist-worn IMU. A novel kinematic variable is proposed, which finds the wrist acceleration in the principal axis (i.e. the direction of the arm swing). This variable (called pca-acc) is obtained by applying sensor fusion on IMU data to find the orientation followed by the use of principal component analysis. An experimental evaluation was performed on 15 healthy young subjects during free walking trials. The experimental results show that the use of the proposed pca-acc variable can significantly improve the walking speed estimation accuracy when compared to the use of raw acceleration information (p<0.01). When Gaussian process regression is used, the resulting walking speed estimation accuracy and precision is about 5.9% and 4.7%, respectively.

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Section: Introductionmentioning

confidence: 99%

Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor

Zihajehzadeh

Park

2016

PLoS ONE

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“…The Kalman Filter is an optimal estimation method under the criterion of minimizing the sum of absolute errors. It has advantages of small amount of computations, strong real-time capability, and can constantly revise estimation value of the future motion state to improve the estimation precision considering real-time and robustness requirements by using the actual motion parameters [15].…”

Section: A the Principle Of Kalman Filtermentioning

confidence: 99%

Calibration of MEMS Accelerometer Based on Kalman Filter and the Improved Six Position Method

Liu¹,

Gaolin²,

Lu³

et al. 2016

JCM

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Abstract-The measurement precision of Micro ElectronicMechanical System (MEMS) accelerometer are influenced by zero drift, scale factor and installation error. In order to increase the measurement accuracy of the MEMS accelerometer, an improved calibration method is proposed on the basis of traditional six position method. The raw measurements of the MEMS accelerometer not divided by the scale factor are filtered by the Kalman Filter first, then the filtered values of six positions are calibrated to obtain the error model of MEMS accelerometer. The error model is verified by the experiments, and the results show that the measurement precision of the calibrated MEMS accelerometer is increased to 0.0066 m/s 2 from the uncalibrated 0.2646 m/s 2 , and the measurement precision of pitch calculated by the calibrated MEMS accelerometer is increased to 0.145 degree. It has been verified that error model based on Kalman Filter and improved six location method are feasible and effective. Therefore, this error model can be very valuable in promoting MEMS accelerometer to be widely used in engineering field.

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“…IMU-based accuracy, however, degrades quickly over time [20] due to the inherent drift depending on the quality of the IMU [21] unless positioning fixes are provided from another source. Integrating GNSS and IMU technologies are used to compensate for standalone deficiencies and to provide better accuracy, reliability, and continuity of the navigation solution [22]. The imaging sensors of mobile mapping systems are time synchronized to GPS time, and thus, the platform georeferencing solution can be easily transferred to imaging data streams, so data can be fused and projected to a global coordinate system [23].…”

Section: Introductionmentioning

confidence: 99%

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

İlçi

Toth

2020

Sensors

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Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

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A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications

Cited by 61 publications

References 31 publications

Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor

Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor

Calibration of MEMS Accelerometer Based on Kalman Filter and the Improved Six Position Method

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

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