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

Duong, Pham Duy; Suh, Young Soo

doi:10.3390/s150715888

Cited by 25 publications

(19 citation statements)

References 20 publications

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“…Then,

is the position of point

in the IMU coordinate frame. The height of the IMU is calculated as [ 21 ]:

and can be rewritten as:

where

is the measurement and

is a measurement noise at the time

and

is:

…”

Section: Virtual Blind Cane Operationmentioning

confidence: 99%

A Virtual Blind Cane Using a Line Laser-Based Vision System and an Inertial Measurement Unit

Dang

Chee

Pham

et al. 2016

Sensors

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A virtual blind cane system for indoor application, including a camera, a line laser and an inertial measurement unit (IMU), is proposed in this paper. Working as a blind cane, the proposed system helps a blind person find the type of obstacle and the distance to it. The distance from the user to the obstacle is estimated by extracting the laser coordinate points on the obstacle, as well as tracking the system pointing angle. The paper provides a simple method to classify the obstacle’s type by analyzing the laser intersection histogram. Real experimental results are presented to show the validity and accuracy of the proposed system.

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“…Then,

is the position of point

in the IMU coordinate frame. The height of the IMU is calculated as [ 21 ]:

and can be rewritten as:

where

is the measurement and

is a measurement noise at the time

and

is:

…”

Section: Virtual Blind Cane Operationmentioning

confidence: 99%

A Virtual Blind Cane Using a Line Laser-Based Vision System and an Inertial Measurement Unit

Dang

Chee

Pham

et al. 2016

Sensors

Self Cite

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“…Data were recorded on one subject while walking. Finally, Duong et al [ 28 ] presented an algorithm for the estimation of a single foot pose by fusing data provided by two IR-ToF sensors and an IMU during different motor tasks (walking, dancing steps, jumping and kicking a ball) performed by a single subject. Unfortunately, none of the abovementioned studies, with the exception of [ 18 ], reported the errors associated with the distance estimated by the specific proximity sensor employed under static and dynamic conditions.…”

Section: Introductionmentioning

confidence: 99%

Static and Dynamic Accuracy of an Innovative Miniaturized Wearable Platform for Short Range Distance Measurements for Human Movement Applications

Bertuletti

Cereatti

Comotti

et al. 2017

Sensors

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Magneto-inertial measurement units (MIMU) are a suitable solution to assess human motor performance both indoors and outdoors. However, relevant quantities such as step width and base of support, which play an important role in gait stability, cannot be directly measured using MIMU alone. To overcome this limitation, we developed a wearable platform specifically designed for human movement analysis applications, which integrates a MIMU and an Infrared Time-of-Flight proximity sensor (IR-ToF), allowing for the estimate of inter-object distance. We proposed a thorough testing protocol for evaluating the IR-ToF sensor performances under experimental conditions resembling those encountered during gait. In particular, we tested the sensor performance for different (i) target colors; (ii) sensor-target distances (up to 200 mm) and (iii) sensor-target angles of incidence (AoI) (up to 60∘). Both static and dynamic conditions were analyzed. A pendulum, simulating the oscillation of a human leg, was used to generate highly repeatable oscillations with a maximum angular velocity of 6 rad/s. Results showed that the IR-ToF proximity sensor was not sensitive to variations of both distance and target color (except for black). Conversely, a relationship between error magnitude and AoI values was found. For AoI equal to 0∘, the IR-ToF sensor performed equally well both in static and dynamic acquisitions with a distance mean absolute error <1.5 mm. Errors increased up to 3.6 mm (static) and 11.9 mm (dynamic) for AoI equal to ±30∘, and up to 7.8 mm (static) and 25.6 mm (dynamic) for AoI equal to ±60∘. In addition, the wearable platform was used during a preliminary experiment for the estimation of the inter-foot distance on a single healthy subject while walking. In conclusion, the combination of magneto-inertial unit and IR-ToF technology represents a valuable alternative solution in terms of accuracy, sampling frequency, dimension and power consumption, compared to existing technologies.

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“…This is the main idea of ZUPT and ZARU. We use a multi-condition algorithm to detect steps using accelerometer and gyroscope readings (Feliz et al, 2009;Won et al, 2004). According to gravity acceleration, the magnitude of the acceleration a k has to be found between two thresholds.…”

Section: Step Detectionmentioning

confidence: 99%

“…Another solution is to combine inertial sensors with distance measurement sensors. In Duong and Suh (2015) the distance sensor measures the distance by measuring the time of flight of infrared light. This sensor is most often used as a proximity sensor in smartphones.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

Uradziński

Guo

2020

J. Sens. Sens. Syst.

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Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

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

Cited by 25 publications

References 20 publications

A Virtual Blind Cane Using a Line Laser-Based Vision System and an Inertial Measurement Unit

A Virtual Blind Cane Using a Line Laser-Based Vision System and an Inertial Measurement Unit

Static and Dynamic Accuracy of an Innovative Miniaturized Wearable Platform for Short Range Distance Measurements for Human Movement Applications

Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

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