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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.


A shoe for the experiment.
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f2-sensors-15-15888: A shoe for the experiment.

Mentions: The proposed algorithm is tested with several foot movement experiments. The foot position is measured with an inertial sensor unit (Xsens MTi) and two distance sensors (VL6180) as in Figure 2. The estimated positions are compared with the positions obtained using an optical motion tracker (Optitrack six Flex 13 camera system), which is considered as a ground truth.


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

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

A shoe for the experiment.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-15-15888: A shoe for the experiment.
Mentions: The proposed algorithm is tested with several foot movement experiments. The foot position is measured with an inertial sensor unit (Xsens MTi) and two distance sensors (VL6180) as in Figure 2. The estimated positions are compared with the positions obtained using an optical motion tracker (Optitrack six Flex 13 camera system), which is considered as a ground truth.

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.