The online estimation of the joint angle based on the gravity acceleration using the accelerometer and gyroscope in the wireless networks

This study aims at the online estimation of the hip and knee angle in the sagittal plane for the motion surveillance only using the tri-axis accelerometers and gyroscopes of IMU, without considering the magnetic disturbance. The proposed method utilizes the projection of gravity acceleration on each sensor coordinate system to estimate the joint angle which rotating around the horizontal axis and approximately horizontal axis. With the third row of the rotation matrix independent of the yaw angle, the proposed method first calculates the projection of the gravity acceleration on each IMU sensor coordinate system only using accelerometer and gyroscope. And then, the rotation matrix between two adjacent coordinate systems is directly calculated. After evaluating the body to sensor rotation matrix, the rotation matrix between two adjacent body segments can be calculated, ultimately. Two types of experiments are adopted in the paper. The results show that the proposed method obtains the outstanding performance with the RMSE lower than 0.8 deg in the horizontal rotation experiment. In the limb joint experiment, the RMSE of the hip joint is lower than 3.12 deg, while the RMSE of the knee joint is lower than 3.83 deg for all predefined locomotion modes. The characteristic of our approach is that it can be run online without any parameter adjustment and additional time latency while ensuring estimation accuracy.

[1]  Seungmin Rho,et al.  High Performance GCM Architecture for the Security of High Speed Network , 2017, International Journal of Parallel Programming.

[2]  Cynthia E Dunning,et al.  Direct comparison of kinematic data collected using an electromagnetic tracking system versus a digital optical system. , 2007, Journal of biomechanics.

[3]  Masahiro Todoh,et al.  Gait posture estimation using wearable acceleration and gyro sensors. , 2009, Journal of biomechanics.

[4]  Eduardo Palermo,et al.  Experimental evaluation of accuracy and repeatability of a novel body-to-sensor calibration procedure for inertial sensor-based gait analysis , 2014 .

[5]  Nora Millor,et al.  Kinematic Parameters to Evaluate Functional Performance of Sit-to-Stand and Stand-to-Sit Transitions Using Motion Sensor Devices: A Systematic Review , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Robert E. Mahony,et al.  Nonlinear Complementary Filters on the Special Orthogonal Group , 2008, IEEE Transactions on Automatic Control.

[7]  Seungmin Rho,et al.  IoT-based personalized NIE content recommendation system , 2018, Multimedia Tools and Applications.

[8]  Aurelio Cappozzo,et al.  Joint kinematics estimate using wearable inertial and magnetic sensing modules. , 2008, Gait & posture.

[9]  Thomas Seel,et al.  Exploiting kinematic constraints to compensate magnetic disturbances when calculating joint angles of approximate hinge joints from orientation estimates of inertial sensors , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

[10]  Chris J. Bleakley,et al.  Accurate Orientation Estimation Using AHRS under Conditions of Magnetic Distortion , 2014, Sensors.

[11]  Tao Liu,et al.  Ambulatory measurement and analysis of the lower limb 3D posture using wearable sensor system , 2009, 2009 International Conference on Mechatronics and Automation.

[12]  M. Shuster,et al.  Three-axis attitude determination from vector observations , 1981 .

[13]  Adriano Ferrari,et al.  ‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors , 2009, Medical & Biological Engineering & Computing.

[14]  Thomas Seel,et al.  IMU-Based Joint Angle Measurement for Gait Analysis , 2014, Sensors.

[15]  Noel C. Perkins,et al.  Method for Estimating Three-Dimensional Knee Rotations Using Two Inertial Measurement Units: Validation with a Coordinate Measurement Machine , 2017, Sensors.

[16]  Awais Ahmad,et al.  MGR: Multi-parameter Green Reliable communication for Internet of Things in 5G network , 2018, J. Parallel Distributed Comput..

[17]  Kamiar Aminian,et al.  A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes , 2005, IEEE Transactions on Biomedical Engineering.

[18]  P. Veltink,et al.  Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Daniel Tik-Pui Fong,et al.  The Use of Wearable Inertial Motion Sensors in Human Lower Limb Biomechanics Studies: A Systematic Review , 2010, Sensors.

[20]  Pietro Picerno,et al.  25 years of lower limb joint kinematics by using inertial and magnetic sensors: A review of methodological approaches. , 2017, Gait & posture.

[21]  Piotr Skrzypczynski,et al.  Performance Comparison of EKF-Based Algorithms for Orientation Estimation on Android Platform , 2015, IEEE Sensors Journal.

[22]  Robert B. McGhee,et al.  A Simplified Quaternion-Based Algorithm for Orientation Estimation From Earth Gravity and Magnetic Field Measurements , 2008, IEEE Transactions on Instrumentation and Measurement.

[23]  D. Roetenberg,et al.  Estimating Body Segment Orientation by Applying Inertial and Magnetic Sensing Near Ferromagnetic Materials , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  W McKinon,et al.  The association between loss of ankle dorsiflexion range of movement, and hip adduction and internal rotation during a step down test. , 2016, Manual therapy.