Gait speed estimation using Kalman Filtering on inertial measurement unit data

2017 ◽  
Author(s):  
Md Nafiul Alam ◽  
Tamanna Tabassum Khan Munia ◽  
Reza Fazel-Rezai
Keyword(s):  
Kalman Filtering ◽  
Gait Speed ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Speed Estimation ◽  
Inertial Measurement

scholarly journals Measuring Gait Velocity and Stride Length with an Ultrawide Bandwidth Local Positioning System and an Inertial Measurement Unit

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2896
Author(s):  
Pratham Singh ◽  
Michael Esposito ◽  
Zach Barrons ◽  
Christian A. Clermont ◽  
John Wannop ◽  
...  
Keyword(s):  
Gold Standard ◽  
Gait Speed ◽  
Inertial Measurement Unit ◽  
Stride Length ◽  
Measurement Unit ◽  
Gait Velocity ◽  
Positioning System ◽  
Inertial Measurement ◽  
Measurement Systems ◽  
Local Positioning System

One possible modality to profile gait speed and stride length includes using wearable technologies. Wearable technology using global positioning system (GPS) receivers may not be a feasible means to measure gait speed. An alternative may include a local positioning system (LPS). Considering that LPS wearables are not good at determining gait events such as heel strikes, applying sensor fusion with an inertial measurement unit (IMU) may be beneficial. Speed and stride length determined from an ultrawide bandwidth LPS equipped with an IMU were compared to video motion capture (i.e., the “gold standard”) as the criterion standard. Ninety participants performed trials at three self-selected walk, run and sprint speeds. After processing location, speed and acceleration data from the measurement systems, speed between the last five meters and stride length in the last stride of the trial were analyzed. Small biases and strong positive intraclass correlations (0.9–1.0) between the LPS and “the gold standard” were found. The significance of the study is that the LPS can be a valid method to determine speed and stride length. Variability of speed and stride length can be reduced when exploring data processing methods that can better extract speed and stride length measurements.

Maximum Correntropy Kalman Filter for Orientation Estimation With Application to LiDAR Inertial Odometry

2020 ◽  
Author(s):  
Seyed Fakoorian ◽  
Matteo Palieri ◽  
Angel Santamaria-Navarro ◽  
Cataldo Guaragnella ◽  
Dan Simon ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Low Cost ◽  
Attitude Estimation ◽  
High Rate ◽  
Measurement Unit ◽  
Orientation Estimation ◽  
Localization Accuracy ◽  
Inertial Measurement ◽  
Robotic Applications

Abstract Accurate attitude estimation using low-cost sensors is an important capability to enable many robotic applications. In this paper, we present a method based on the concept of correntropy in Kalman filtering to estimate the 3D orientation of a rigid body using a low-cost inertial measurement unit (IMU). We then leverage the proposed attitude estimation framework to develop a LiDAR-Intertial Odometry (LIO) demonstrating improved localization accuracy with respect to traditional methods. This is of particular importance when the robot undergoes high-rate motions that typically exacerbate the issues associated with low-cost sensors. The proposed orientation estimation approach is first validated using the data coming from a low-cost IMU sensor. We further demonstrate the performance of the proposed LIO solution in a simulated robotic cave exploration scenario.

Walking speed estimation using a shank-mounted inertial measurement unit

2010 ◽  
Vol 43 (8) ◽  
pp. 1640-1643 ◽  
Author(s):  
Q. Li ◽  
M. Young ◽  
V. Naing ◽  
J.M. Donelan
Keyword(s):  
Inertial Measurement Unit ◽  
Walking Speed ◽  
Measurement Unit ◽  
Speed Estimation ◽  
Inertial Measurement

ERROR ANALYSIS OF THE INERTIAL MEASUREMENT UNIT / INERCINIO MATAVIMŲ BLOKO KLAIDŲ ANALIZĖ

Aviation ◽  
2011 ◽  
Vol 15 (1) ◽  
pp. 5-10
Author(s):  
František Adamcík
Keyword(s):  
Signal Processing ◽  
Error Analysis ◽  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Allan Variance ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Navigation Solution ◽  
Sensor Errors

The paper describes the results of error analysis of the new inertial measurement unit ADIS16364 produced by Analog Devices. This error analysis concerns stochastic sensor errors identified by the Allan variance method. In order to improve the performance of the inertial sensors, the users are keen to know more details about the noise components in each axis for a better modelling of the stochastic parts to improve the navigation solution. The main contribution of this paper is to present data necessary for further inertial sensors signal processing by means of Kalman filtering. Santrauka Straipsnyje pateikiami naujojo inercinio matavimų bloko ADIS16364 klaidų analizės rezultatai. Aprašyta klaidų analizė yra susijusi su stochastinio jutiklio klaidomis, nustatytomis Allano variacijos metodu. Siekiant pagerinti inercinių jutiklių našumą, naudotojai yra linkę daugiau sužinoti apie kiekvienoje ašyje esančius komponentus, kad būtų pagerintas stochastinių dalių modeliavimas bei rasti pažangesni navigacijos sprendimai. Šiuo darbu siekiama pristatyti duomenis, kurie yra reikalingi tolimesniam inercinių jutiklių signalų apdorojimui panaudojant Kalmano filtravimą.

A Comprehensive Overview of Inertial Sensor Calibration Techniques

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Shashi Poddar ◽  
Vipan Kumar ◽  
Amod Kumar
Keyword(s):  
High Precision ◽  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Comprehensive Overview ◽  
Inertial Measurement ◽  
Calibration Techniques

Inertial measurement unit (IMU) comprising of the accelerometer and gyroscope is prone to various deterministic errors like bias, scale factor, and nonorthogonality, which need to be calibrated carefully. In this paper, a survey has been carried out over different calibration techniques that try to estimate these error parameters. These calibration schemes are discussed under two broad categories, that is, calibration with high-end equipment and without any equipment. Traditional calibration techniques use high-precision equipment to generate references for calibrating inertial sensors and are generally laboratory-based setup. Inertial sensor calibration without the use of any costly equipment is further studied under two subcategories: ones based on multiposition method and others with Kalman filtering framework. Later, a brief review of vision-based inertial sensor calibration schemes is also provided in this work followed by a discussion which indicates different shortcomings and future scopes in the area of inertial sensor calibration.

scholarly journals Novel indoor positioning algorithm based on Lidar/inertial measurement unit integrated system

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199992
Author(s):  
Ping Jiang ◽  
Liang Chen ◽  
Hang Guo ◽  
Min Yu ◽  
Jian Xiong
Keyword(s):  
Mobile Robot ◽  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Simultaneous Localization And Mapping ◽  
Measurement Unit ◽  
Accuracy Rate ◽  
Inertial Measurement ◽  
Mean Error ◽  
Localization And Mapping ◽  
The Mean

As an important research field of mobile robot, simultaneous localization and mapping technology is the core technology to realize intelligent autonomous mobile robot. Aiming at the problems of low positioning accuracy of Lidar (light detection and ranging) simultaneous localization and mapping with nonlinear and non-Gaussian noise characteristics, this article presents a mobile robot simultaneous localization and mapping method that combines Lidar and inertial measurement unit to set up a multi-sensor integrated system and uses a rank Kalman filtering to estimate the robot motion trajectory through inertial measurement unit and Lidar observations. Rank Kalman filtering is similar to the Gaussian deterministic point sampling filtering algorithm in structure, but it does not need to meet the assumptions of Gaussian distribution. It completely calculates the sampling points and the sampling points weights based on the correlation principle of rank statistics. It is suitable for nonlinear and non-Gaussian systems. With multiple experimental tests of small-scale arc trajectories, we can see that compared with the alone Lidar simultaneous localization and mapping algorithm, the new algorithm reduces the mean error of the indoor mobile robot in the X direction from 0.0928 m to 0.0451 m, with an improved accuracy rate of 46.39%, and the mean error in the Y direction from 0.0772 m to 0.0405 m, which improves the accuracy rate of 48.40%. Compared with the extended Kalman filter fusion algorithm, the new algorithm reduces the mean error of the indoor mobile robot in the X direction from 0.0597 m to 0.0451 m, with an improved accuracy rate of 24.46%, and the mean error in the Y direction from 0.0537 m to 0.0405 m, which improves the accuracy rate of 24.58%. Finally, we also tested on a large-scale rectangular trajectory, compared with the extended Kalman filter algorithm, rank Kalman filtering improves the accuracy of 23.84% and 25.26% in the X and Y directions, respectively, it is verified that the accuracy of the algorithm proposed in this article has been improved.

scholarly journals Walking-speed estimation using a single inertial measurement unit for the older adults

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0227075
Author(s):  
Seonjeong Byun ◽  
Hyang Jun Lee ◽  
Ji Won Han ◽  
Jun Sung Kim ◽  
Euna Choi ◽  
...  
Keyword(s):  
Older Adults ◽  
Inertial Measurement Unit ◽  
Walking Speed ◽  
Measurement Unit ◽  
Speed Estimation ◽  
Inertial Measurement

Research on the Temperature Compensation Method for Micro Inertial Measurement Unit

2013 ◽  
Vol 823 ◽  
pp. 228-231
Author(s):  
Dan Wang ◽  
Guo Wei Gao ◽  
Fang Wei Cui ◽  
Zhen Song
Keyword(s):  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Temperature Compensation ◽  
Stepwise Regression ◽  
Measurement Accuracy ◽  
Measurement Unit ◽  
Zero Bias ◽  
Inertial Measurement ◽  
Temperature Characteristics ◽  
Multiple Variables

In this paper, the temperature characteristics of Micro Inertial Measurement Unit (MIMU) were studied for improving its stability. The temperature error model was built on the basis of analyzing its temperature characteristics. The model combined zero bias and calibration factors for MEMS accelerometers and gyros with multiple variables and it was optimized by the stepwise regression method. Meanwhile, combined with Kalman filtering theory, using Kalman filtering to reduce the noise of the temperature measurement could further improve the compensation accuracy and measurement accuracy.

Sign in / Sign up

Export Citation Format

Share Document