scholarly journals Magnetic Condition-Independent 3D Joint Angle Estimation Using Inertial Sensors and Kinematic Constraints

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5522 ◽  
Author(s):  
Jung Keun Lee ◽  
Tae Hyeong Jeon
Keyword(s):  
Joint Angle ◽  
Inertial Sensors ◽  
Three Dimensional ◽  
Estimation Method ◽  
Direction Cosine ◽  
Magnetic Disturbance ◽  
Angle Estimation ◽  
Magnetic Condition ◽  
Measurement Units ◽  
Heading Estimation

In biomechanics, joint angle estimation using wearable inertial measurement units (IMUs) has been getting great popularity. However, magnetic disturbance issue is considered problematic as the disturbance can seriously degrade the accuracy of the estimated joint angles. This study proposes a magnetic condition-independent three-dimensional (3D) joint angle estimation method based on IMU signals. The proposed method is implemented in a sequential direction cosine matrix-based orientation Kalman filter (KF), which is composed of an attitude estimation KF followed by a heading estimation KF. In the heading estimation KF, an acceleration-level kinematic constraint from a spherical joint replaces the magnetometer signals for the correction procedure. Because the proposed method does not rely on the magnetometer, it is completely magnetic condition-independent and is not affected by the magnetic disturbance. For the averaged root mean squared errors of the three tests performed using a rigid two-link system, the proposed method produced 1.58°, while the conventional method with the magnetic disturbance compensation mechanism produced 5.38°, showing a higher accuracy of the proposed method in the magnetically disturbed conditions. Due to the independence of the proposed method from the magnetic condition, the proposed approach could be reliably applied in various fields that require robust 3D joint angle estimation through IMU signals in an unspecified arbitrary magnetic environment.

A Joint-Angle Estimation Method for Industrial Manipulators Using Inertial Sensors

2015 ◽  
Vol 20 (5) ◽  
pp. 2486-2495 ◽  
Author(s):  
Luciano Cantelli ◽  
Giovanni Muscato ◽  
Marco Nunnari ◽  
Davide Spina
Keyword(s):  
Joint Angle ◽  
Inertial Sensors ◽  
Estimation Method ◽  
Angle Estimation ◽  
Industrial Manipulators

scholarly journals A Comparative Study of Markerless Systems Based on Color-Depth Cameras, Polymer Optical Fiber Curvature Sensors, and Inertial Measurement Units: Towards Increasing the Accuracy in Joint Angle Estimation

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 173 ◽  
Author(s):  
Nicolas Valencia-Jimenez ◽  
Arnaldo Leal-Junior ◽  
Leticia Avellar ◽  
Laura Vargas-Valencia ◽  
Pablo Caicedo-Rodríguez ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Correlation Coefficient ◽  
Joint Angle ◽  
Vision System ◽  
Inertial Measurement Units ◽  
Polymer Optical Fiber ◽  
Angle Estimation ◽  
Inertial Measurement ◽  
Non Invasive ◽  
Measurement Units

This paper presents a comparison between a multiple red green blue-depth (RGB-D) vision system, an intensity variation-based polymer optical fiber (POF) sensor, and inertial measurement units (IMUs) for human joint angle estimation and movement analysis. This systematic comparison aims to study the trade-off between the non-invasive feature of a vision system and its accuracy with wearable technologies for joint angle measurements. The multiple RGB-D vision system is composed of two camera-based sensors, in which a sensor fusion algorithm is employed to mitigate occlusion and out-range issues commonly reported in such systems. Two wearable sensors were employed for the comparison of angle estimation: (i) a POF curvature sensor to measure 1-DOF angle; and (ii) a commercially available IMUs MTw Awinda from Xsens. A protocol to evaluate elbow joints of 11 healthy volunteers was implemented and the comparison of the three systems was presented using the correlation coefficient and the root mean squared error (RMSE). Moreover, a novel approach for angle correction of markerless camera-based systems is proposed here to minimize the errors on the sagittal plane. Results show a correlation coefficient up to 0.99 between the sensors with a RMSE of 4.90 ∘ , which represents a two-fold reduction when compared with the uncompensated results (10.42 ∘ ). Thus, the RGB-D system with the proposed technique is an attractive non-invasive and low-cost option for joint angle assessment. The authors envisage the proposed vision system as a valuable tool for the development of game-based interactive environments and for assistance of healthcare professionals on the generation of functional parameters during motion analysis in physical training and therapy.

scholarly journals Finger joint angle estimation based on sEMG signals by Attention-MLP

2021 ◽  
Vol 2113 (1) ◽  
pp. 012081
Author(s):  
Zhebin Yu ◽  
Hui Wang ◽  
Wenlong Yu
Keyword(s):  
Joint Angle ◽  
Pearson Correlation ◽  
Estimation Method ◽  
Robotic Arm ◽  
Joint Angles ◽  
Angle Estimation ◽  
Interactive Field ◽  
Robotic Arm Control ◽  
Better Than ◽  
Semg Signals

Abstract sEMG(Surface electromyography) signal was widely applied in human-machine interactive field, especially in robotic arm control. In this paper, we built the Attention-MLP (Multilayer Perceptron) model to implement a type of continuous joint angle estimation method based on sEMG for six grasp movements, we tested this model on Ninapro dataset and the average Pearson correlation coefficient (CC) and the average root mean square error (RMSE) of the proposed Attention-MLP method achieved 0.812±0.02 and 10.51±1.98; the average CC and RMSE of this method are better than Sparse Pseudo-input Gaussian processes (SPGP), its average CC and RMSE are 12.14±2.30 and 0.727±0.07. Compared with the traditional method SPGP, our model performed better on continuously estimation of ten main hand joint angles under 6 grip movements.

Knee joint angle estimation during gait cycle using low-cost inertial sensors

2017 ◽  
Author(s):  
F. Sanchez-Guzman ◽  
J. F. Guerrero-Castellanos ◽  
G. Mino-Aguilar ◽  
R. C. Ambrosio-Lazaro ◽  
J. Linares-Flores
Keyword(s):  
Knee Joint ◽  
Gait Cycle ◽  
Joint Angle ◽  
Inertial Sensors ◽  
Low Cost ◽  
Angle Estimation ◽  
Knee Joint Angle

scholarly journals Automatic Orientation Estimation of Inertial Sensors in C-Arm CT Projections

2019 ◽  
Vol 5 (1) ◽  
pp. 195-198 ◽  
Author(s):  
Mareike Thies ◽  
Jennifer Maier ◽  
Björn Eskofier ◽  
Andreas Maier ◽  
Marc Levenston ◽  
...  
Keyword(s):  
Coordinate System ◽  
Inertial Sensors ◽  
Local Coordinate System ◽  
Three Dimensional ◽  
Ground Truth ◽  
Radial Symmetry ◽  
Sift Algorithm ◽  
Position Data ◽  
Measurement Units ◽  
Three Dimensional Coordinate

AbstractTo obtain CT images of the knee joint in a more lifelike position, data acquisition can be performed with patients in standing rather than in lying position. However, in that situation, people tend to show involuntary motion. One possibility to compensate for that motion is the use of Inertial Measurement Units, that capture the accelerations during the scan. For this purpose, their local coordinate system needs to be known. An estimation based on the SIFT algorithm was implemented and compared to an existing approach that uses the Fast Radial Symmetry transform and to expert labels for evaluation. The SIFT method showed to be superior to the existing approach as it could extract stable feature points from the projections that were used to estimate the three-dimensional coordinate system in a reliable manner. The final algorithm achieved a mean euclidean distance of 2.61 mm between the calculated position of the origin and the assumed ground truth by the expert labels.

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