Human Joint Angle Estimation with Inertial Sensors and Validation with A Robot Arm

2015 ◽  
Vol 62 (7) ◽  
pp. 1759-1767 ◽  
Author(s):  
Mahmoud El-Gohary ◽  
James McNames
2018 ◽  
Vol 30 (8) ◽  
pp. 1787
Author(s):  
Sang-Ho Han ◽  
Mun-Ho Ryu ◽  
Je-Nam Kim

2015 ◽  
Vol 20 (5) ◽  
pp. 2486-2495 ◽  
Author(s):  
Luciano Cantelli ◽  
Giovanni Muscato ◽  
Marco Nunnari ◽  
Davide Spina

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5522 ◽  
Author(s):  
Jung Keun Lee ◽  
Tae Hyeong Jeon

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.


Author(s):  
Lina Tong ◽  
Feng Zhang ◽  
Zeng-Guang Hou ◽  
Weiqun Wang ◽  
Liang Peng

Author(s):  
F. Sanchez-Guzman ◽  
J. F. Guerrero-Castellanos ◽  
G. Mino-Aguilar ◽  
R. C. Ambrosio-Lazaro ◽  
J. Linares-Flores

Author(s):  
Alp Guler ◽  
Nikolaos Kardaris ◽  
Siddhartha Chandra ◽  
Vassilis Pitsikalis ◽  
Christian Werner ◽  
...  

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