Abstract
This paper investigates the generation of simulation data for motion estimation using inertial sensors. The smoothing algorithm with waypoint-based map matching is proposed using foot-mounted inertial sensors to estimate position and attitude. The simulation data are generated using spline functions, where the estimated position and attitude are used as control points. The attitude is represented using B-spline quaternion and the position is represented by eighth-order algebraic splines. The simulation data can be generated using inertial sensors (accelerometer and gyroscope) without using any additional sensors. Through indoor experiments, two scenarios were examined include 2D walking path (rectangular) and 3D walking path (corridor and stairs) for simulation data generation. The proposed simulation data is used to evaluate the estimation performance with different parameters such as different noise levels and sampling periods.
Highlights
Motion estimation using inertial sensors is one of the most important research topics that is increasingly applied in many application areas such as medical applications, sports, and entertainment [1]
An optical motion tracking is used as a reference to compare with motion estimation using inertial sensors in [15]
The simulation data are generated using spline functions, where the estimated position and attitude are used as control points
Summary
Motion estimation using inertial sensors is one of the most important research topics that is increasingly applied in many application areas such as medical applications, sports, and entertainment [1]. Inertial measurement unit (IMU) sensors are commonly used to estimate human motion [2,3,4,5]. If additional sensors (other than inertial sensors) are used, sensor fusion algorithms [12,13] can be used to obtain more accurate motion estimation. The estimated value is usually verified through experiments with both IMU and optical motion tracker [14,15]. In [2], the Vicon optical motion capture system is used to evaluate the effectiveness of human pose estimation method and its associated sensor calibration procedure. An optical motion tracking is used as a reference to compare with motion estimation using inertial sensors in [15]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
