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Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors.

Duong PD, Suh YS - Sensors (Basel) (2015)

Bottom Line: The distance sensor is a time-of-flight range finder and can measure distance up to 20 cm.A Kalman filter with 21 states is proposed to estimate both the calibration parameter (relative pose of distance sensors with respect to the inertial sensor unit) and foot pose.Once the calibration parameter is obtained, a Kalman filter with nine states can be used to estimate foot pose.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, University of Ulsan, Namgu, Ulsan 680-749, Korea. duyduongd2@gmail.com.

ABSTRACT
There are many inertial sensor-based foot pose estimation algorithms. In this paper, we present a methodology to improve the accuracy of foot pose estimation using two low-cost distance sensors (VL6180) in addition to an inertial sensor unit. The distance sensor is a time-of-flight range finder and can measure distance up to 20 cm. A Kalman filter with 21 states is proposed to estimate both the calibration parameter (relative pose of distance sensors with respect to the inertial sensor unit) and foot pose. Once the calibration parameter is obtained, a Kalman filter with nine states can be used to estimate foot pose. Through four activities (walking, dancing step, ball kicking, jumping), it is shown that the proposed algorithm significantly improves the vertical position estimation.

No MeSH data available.


Ball kicking estimation: proposed method.
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f6-sensors-15-15888: Ball kicking estimation: proposed method.

Mentions: Another example is given in Figures 6 and 7, where a ball kicking action is done. The x and y position estimation results are similar, both by the proposed method and K.F. (zero velocity + height updating). In this case, the position errors are quite large, presumably due to the fact that there is a rather long moving interval (2.8∼4-s interval) and a quick movement (large sensor values). In the z axis position estimation, we can see that the proposed method gives a significantly better result.


Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors.

Duong PD, Suh YS - Sensors (Basel) (2015)

Ball kicking estimation: proposed method.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541859&req=5

f6-sensors-15-15888: Ball kicking estimation: proposed method.
Mentions: Another example is given in Figures 6 and 7, where a ball kicking action is done. The x and y position estimation results are similar, both by the proposed method and K.F. (zero velocity + height updating). In this case, the position errors are quite large, presumably due to the fact that there is a rather long moving interval (2.8∼4-s interval) and a quick movement (large sensor values). In the z axis position estimation, we can see that the proposed method gives a significantly better result.

Bottom Line: The distance sensor is a time-of-flight range finder and can measure distance up to 20 cm.A Kalman filter with 21 states is proposed to estimate both the calibration parameter (relative pose of distance sensors with respect to the inertial sensor unit) and foot pose.Once the calibration parameter is obtained, a Kalman filter with nine states can be used to estimate foot pose.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, University of Ulsan, Namgu, Ulsan 680-749, Korea. duyduongd2@gmail.com.

ABSTRACT
There are many inertial sensor-based foot pose estimation algorithms. In this paper, we present a methodology to improve the accuracy of foot pose estimation using two low-cost distance sensors (VL6180) in addition to an inertial sensor unit. The distance sensor is a time-of-flight range finder and can measure distance up to 20 cm. A Kalman filter with 21 states is proposed to estimate both the calibration parameter (relative pose of distance sensors with respect to the inertial sensor unit) and foot pose. Once the calibration parameter is obtained, a Kalman filter with nine states can be used to estimate foot pose. Through four activities (walking, dancing step, ball kicking, jumping), it is shown that the proposed algorithm significantly improves the vertical position estimation.

No MeSH data available.