Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation

The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60∘. In the second, we evaluated our system in a real scenario, evaluating the knee of a healthy adult individual during gait. In the third experiment, we evaluated the software using data from gold standard devices, comparing the results of our software with Ground Truth. In the evaluation of the Lokomat, our system achieved an average ROM of 58.28∘, and during evaluation in a real scenario it achieved an average ROM of 44.62∘. In comparing our software with Ground Truth, we achieved a root-mean-square error of 0.04 and a mean average percentage error of 2.95%. These results encourage the use of this system in other scenarios.

[1]  Jung Keun Lee,et al.  IMU-Based Joint Angle Estimation Under Various Walking and Running Conditions , 2018, Journal of the Korean Society for Precision Engineering.

[2]  Sebastian O. H. Madgwick,et al.  An efficient orientation filter for inertial and inertial / magnetic sensor arrays , 2010 .

[3]  Guang-Zhong Yang,et al.  Micromagnetometer Calibration for Accurate Orientation Estimation , 2015, IEEE Transactions on Biomedical Engineering.

[4]  M. Morari,et al.  Robotic Orthosis Lokomat: A Rehabilitation and Research Tool , 2003, Neuromodulation : journal of the International Neuromodulation Society.

[5]  Shyamal Patel,et al.  GaitPy: An Open-Source Python Package for Gait Analysis Using an Accelerometer on the Lower Back , 2019, J. Open Source Softw..

[6]  Guang-Zhong Yang,et al.  Gait Analysis From a Single Ear-Worn Sensor: Reliability and Clinical Evaluation for Orthopaedic Patients , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[8]  Robert Gailey,et al.  A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices , 2018, Sensors.

[9]  Hongnian Yu,et al.  An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors , 2018, Sensors.

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

[11]  Philippe Mahaudens,et al.  Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations , 2020, Sensors.

[12]  Albert Busquets,et al.  Validity and reliability of the Kinovea program in obtaining angles and distances using coordinates in 4 perspectives , 2019, PloS one.

[13]  Thomas Seel,et al.  On Motions That Allow for Identification of Hinge Joint Axes from Kinematic Constraints and 6D IMU Data , 2019, 2019 18th European Control Conference (ECC).

[14]  Charles Malleson,et al.  Total Capture: 3D Human Pose Estimation Fusing Video and Inertial Sensors , 2017, BMVC.

[15]  Clare Strongman,et al.  A scoping review of non-linear analysis approaches measuring variability in gait due to lower body injury or dysfunction. , 2020, Human movement science.

[16]  Alessio Caroselli,et al.  Quasi-Real Time Estimation of Angular Kinematics Using Single-Axis Accelerometers , 2013, Sensors.

[17]  Johan Bellemans,et al.  Functional movement assessment by means of inertial sensor technology to discriminate between movement behaviour of healthy controls and persons with knee osteoarthritis , 2020, Journal of NeuroEngineering and Rehabilitation.

[18]  Kwok-Wing Chau,et al.  A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources , 2019, IEEE Access.

[19]  Lynn Huh,et al.  Kalman Filter for Beginners: with MATLAB Examples , 2011 .

[20]  Thurmon E Lockhart,et al.  Fall Risk Assessments Based on Postural and Dynamic Stability Using Inertial Measurement Unit , 2012, Safety and health at work.

[21]  Bo Tao,et al.  Spatiotemporal Modeling for Nonlinear Distributed Thermal Processes Based on KL Decomposition, MLP and LSTM Network , 2020, IEEE Access.

[22]  K. Aminian,et al.  Ambulatory measurement of 3D knee joint angle. , 2008, Journal of biomechanics.

[23]  Hongnian Yu,et al.  Gait quantification and visualization for digital healthcare , 2020 .

[24]  Henri De Vroey,et al.  Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review , 2020, Sensors.

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

[26]  N. Lovell,et al.  Quaternion-Based Complementary Filter for Attitude Determination of a Smartphone , 2016, IEEE Sensors Journal.

[27]  Chai Kiat Yeo,et al.  Smartphone Orientation Estimation Algorithm Combining Kalman Filter With Gradient Descent , 2018, IEEE Journal of Biomedical and Health Informatics.

[28]  Simone A. Ludwig,et al.  Optimization of gyroscope and accelerometer/magnetometer portion of basic attitude and heading reference system , 2018, 2018 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL).

[29]  Simone A. Ludwig,et al.  Comparison of attitude and heading reference systems using foot mounted MIMU sensor data: basic, Madgwick, and Mahony , 2018, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[30]  Henri De Vroey,et al.  A Novel Method to Estimate the Full Knee Joint Kinematics Using Low Cost IMU Sensors for Easy to Implement Low Cost Diagnostics , 2020, Sensors.

[31]  Thomas Seel,et al.  Joint axis and position estimation from inertial measurement data by exploiting kinematic constraints , 2012, 2012 IEEE International Conference on Control Applications.

[32]  Jianguo Yan,et al.  Kalman filtering parameter optimization techniques based on genetic algorithm , 2008, 2008 IEEE International Conference on Automation and Logistics.

[33]  Andrew E Anderson,et al.  Soft tissue artifact causes underestimation of hip joint kinematics and kinetics in a rigid-body musculoskeletal model. , 2020, Journal of biomechanics.

[34]  Ling Shi,et al.  Drift-Free and Self-Aligned IMU-Based Human Gait Tracking System With Augmented Precision and Robustness , 2020, IEEE Robotics and Automation Letters.

[35]  C. Gühmann,et al.  RIANN—A Robust Neural Network Outperforms Attitude Estimation Filters , 2021, AI.

[36]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[37]  Andreas Hein,et al.  Towards an Automated Unsupervised Mobility Assessment for Older People Based on Inertial TUG Measurements , 2018, Sensors.

[38]  Amin Taheri-Garavand,et al.  Deep learning-based appearance features extraction for automated carp species identification , 2020 .

[39]  Ettore Pennestrì,et al.  ESA multibody simulator for spacecrafts’ ascent and landing in a microgravity environment , 2015 .