An adaptive complementary filter for inertial sensor based data fusion to track upper body motion

Author(s):  
M. Sajeewani Karunarathne ◽  
Samitha W. Ekanayake ◽  
Pubudu N. Pathirana
Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1390
Author(s):  
Tomasz Ursel ◽  
Michał Olinski

This article aims to develop a system capable of estimating the displacement of a moving object with the usage of a relatively cheap and easy to apply sensors. There is a growing need for such systems, not only for robots, but also, for instance, pedestrian navigation. In this paper, the theory for this idea, including data postprocessing algorithms for a MEMS accelerometer and an optical flow sensor (OFS), as well as the developed complementary filter applied for sensor fusion, are presented. In addition, a vital part of the accelerometer’s algorithm, the zero velocity states detection, is implemented. It is based on analysis of the acceleration’s signal and further application of acceleration symmetrization, greatly improving the obtained displacement. A test stand with a linear guide and motor enabling imposing a specified linear motion is built. The results of both sensors’ testing suggest that the displacement estimated by each of them is highly correct. Fusion of the sensors’ data gives even better outcomes, especially in cases with external disturbance of OFS. The comparative evaluation of estimated linear displacements, in each case related to encoder data, confirms the algorithms’ operation correctness and proves the chosen sensors’ usefulness in the development of a linear displacement measuring system.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ramon J. Boekesteijn ◽  
José M. H. Smolders ◽  
Vincent J. J. F. Busch ◽  
Alexander C. H. Geurts ◽  
Katrijn Smulders

Abstract Background Although it is well-established that osteoarthritis (OA) impairs daily-life gait, objective gait assessments are not part of routine clinical evaluation. Wearable inertial sensors provide an easily accessible and fast way to routinely evaluate gait quality in clinical settings. However, during these assessments, more complex and meaningful aspects of daily-life gait, including turning, dual-task performance, and upper body motion, are often overlooked. The aim of this study was therefore to investigate turning, dual-task performance, and upper body motion in individuals with knee or hip OA in addition to more commonly assessed spatiotemporal gait parameters using wearable sensors. Methods Gait was compared between individuals with unilateral knee (n = 25) or hip OA (n = 26) scheduled for joint replacement, and healthy controls (n = 27). For 2 min, participants walked back and forth along a 6-m trajectory making 180° turns, with and without a secondary cognitive task. Gait parameters were collected using 4 inertial measurement units on the feet and trunk. To test if dual-task gait, turning, and upper body motion had added value above spatiotemporal parameters, a factor analysis was conducted. Effect sizes were computed as standardized mean difference between OA groups and healthy controls to identify parameters from these gait domains that were sensitive to knee or hip OA. Results Four independent domains of gait were obtained: speed-spatial, speed-temporal, dual-task cost, and upper body motion. Turning parameters constituted a gait domain together with cadence. From the domains that were obtained, stride length (speed-spatial) and cadence (speed-temporal) had the strongest effect sizes for both knee and hip OA. Upper body motion (lumbar sagittal range of motion), showed a strong effect size when comparing hip OA with healthy controls. Parameters reflecting dual-task cost were not sensitive to knee or hip OA. Conclusions Besides more commonly reported spatiotemporal parameters, only upper body motion provided non-redundant and sensitive parameters representing gait adaptations in individuals with hip OA. Turning parameters were sensitive to knee and hip OA, but were not independent from speed-related gait parameters. Dual-task parameters had limited additional value for evaluating gait in knee and hip OA, although dual-task cost constituted a separate gait domain. Future steps should include testing responsiveness of these gait domains to interventions aiming to improve mobility.


Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.


2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Yiting Liu ◽  
Lihui Wang

Two viewpoints are given: (1) initial alignment of strapdown inertial navigation system (SINS) can be fulfilled with a set of inertial sensor data; (2) estimation time for sensor errors can be shortened by repeated data fusion on the added backward-forward SINS resolution results and the external reference data. Based on the above viewpoints, aiming to estimate gyro bias in a shortened time, a rapid transfer alignment method, without any changes for Kalman filter, is introduced. In this method, inertial sensor data and reference data in one reference data update cycle are stored, and one backward and one forward SINS resolutions are executed. Meanwhile, data fusion is executed when the corresponding resolution ends. With the added backward-forward SINS resolution, in the above mentioned update cycle, the estimating operations for gyro bias are added twice, and the estimation time for it is shortened. In the ship swinging condition, with the “velocity plus yaw” matching, the effectiveness of this method is proved by the simulation.


Robotics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 33
Author(s):  
Elisa Digo ◽  
Mattia Antonelli ◽  
Valerio Cornagliotto ◽  
Stefano Pastorelli ◽  
Laura Gastaldi

(1) Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick and place movements and to analyze the collected features from the future perspective of collaborative robotic applications and human motion prediction algorithms. (2) Methods: Inertial Measurement Units and a stereophotogrammetric system were adopted to track the upper body motion of 10 healthy young subjects performing pick and place operations at three different heights. From the obtained database, 10 features were selected and used to distinguish among pick and place gestures at different heights. Classification performances were evaluated by estimating confusion matrices and F1-scores. (3) Results: Values on matrices diagonals were definitely greater than those in other positions. Furthermore, F1-scores were very high in most cases. (4) Conclusions: Upper arm longitudinal acceleration and markers coordinates of wrists and elbows could be considered representative features of pick and place gestures at different heights, and they are consequently suitable for the definition of a human motion prediction algorithm to be adopted in effective collaborative robotics industrial applications.


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