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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.


Walking position estimation: K.F. (zero velocity + height updating).
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f5-sensors-15-15888: Walking position estimation: K.F. (zero velocity + height updating).

Mentions: In Figures 3, 4–5, the estimated positions (walking case) by three methods (the proposed method and the two inertial sensor-only methods) are given along with the position by an optical tracker. Since the optical tracker coordinate system is different from the world coordinate system, the position data are translated and rotated. Furthermore, the inertial sensor data and optical tracker data are not synchronized at the hardware level. The data are synchronized by maximizing the cross-correlation.


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

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

Walking position estimation: K.F. (zero velocity + height updating).
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-15-15888: Walking position estimation: K.F. (zero velocity + height updating).
Mentions: In Figures 3, 4–5, the estimated positions (walking case) by three methods (the proposed method and the two inertial sensor-only methods) are given along with the position by an optical tracker. Since the optical tracker coordinate system is different from the world coordinate system, the position data are translated and rotated. Furthermore, the inertial sensor data and optical tracker data are not synchronized at the hardware level. The data are synchronized by maximizing the cross-correlation.

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.