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 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.

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

Sensorless Angle Estimation Method for Brushed DC Motor using Impedance Variation by Contact Switching

2017 ◽  
Vol 137 (11) ◽  
pp. 827-836
Author(s):  
Yuki Saito ◽  
Kazuma Nakai ◽  
Hiromu Sekiguchi ◽  
Satoshi Fukushima ◽  
Takahiro Nozaki ◽  
...  
Keyword(s):  
Estimation Method ◽  
Dc Motor ◽  
Angle Estimation ◽  
Impedance Variation
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