Influence of IMU position and orientation placement errors on ground reaction force estimation.

Wearable inertial measurement units (IMU) have been proposed to estimate GRF outside of specialized laboratories, however the precise influence of sensor placement error on accuracy is unknown. We investigated the influence of IMU position and orientation placement errors on GRF estimation accuracy. METHODS Kinematic data from twelve healthy subjects based on marker trajectories were used to simulate 1848 combinations of sensor position placement errors (range ± 100 mm) and orientation placement errors (range ± 25°) across eight body segments (trunk, pelvis, left/right thighs, left/right shanks, and left/right feet) during normal walking trials for baseline cases when a single sensor was misplaced and for the extreme cases when all sensors were simultaneously misplaced. Three machine learning algorithms were used to estimate GRF for each placement error condition and compared with the no placement error condition to evaluate performance. RESULTS Position placement errors for a single misplaced IMU reduced vertical GRF (VGRF), medio-lateral GRF (MLGRF), and anterior-posterior GRF (APGRF) estimation accuracy by up to 1.1%, 2.0%, and 0.9%, respectively and for all eight simultaneously misplaced IMUs by up to 4.9%, 6.0%, and 4.3%, respectively. Orientation placement errors for a single misplaced IMU reduced VGRF, MLGRF, and APGRF estimation accuracy by up to 4.8%, 7.3%, and 1.5%, respectively and for all eight simultaneously misplaced IMUs by up to 20.8%, 23.4%, and 12.3%, respectively. CONCLUSION IMU sensor misplacement, particularly orientation placement errors, can significantly reduce GRF estimation accuracy and thus measures should be taken to account for placement errors in implementations of GRF estimation via wearable IMUs.

[1]  Bertram Taetz,et al.  Towards self-calibrating inertial body motion capture , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[2]  R. Marcus,et al.  Comparison of 2 Forms of Kinetic Biofeedback on the Immediate Correction of Knee Extensor Moment Asymmetry Following Total Knee Arthroplasty During Decline Walking , 2019, The Journal of orthopaedic and sports physical therapy.

[3]  D. K. Arvind,et al.  IMUSim: A simulation environment for inertial sensing algorithm design and evaluation , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[4]  Jurandir Nadal,et al.  Residual analysis of ground reaction forces simulation during gait using neural networks with different configurations , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[5]  D. Roetenberg,et al.  Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors , 2009 .

[6]  Eduardo Rocon,et al.  An IMU-to-Body Alignment Method Applied to Human Gait Analysis , 2016, Sensors.

[7]  Jason K. Moore,et al.  An elaborate data set on human gait and the effect of mechanical perturbations , 2015, PeerJ.

[8]  Çağatay Berke Erdaş,et al.  Parkinson's disease monitoring from gait analysis via foot-worn sensors , 2018 .

[9]  Jaap H. Buurke,et al.  Ambulatory assessment of walking balance after stroke using instrumented shoes , 2016, Journal of NeuroEngineering and Rehabilitation.

[10]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[11]  Billur Barshan,et al.  Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors , 2017, Sensors.

[12]  Zhelong Wang,et al.  A method to calibrate installation orientation errors of inertial sensors for gait analysis , 2014, 2014 IEEE International Conference on Information and Automation (ICIA).

[13]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[14]  Stephen A. Billings,et al.  Real-Life Measurement of Tri-Axial Walking Ground Reaction Forces Using Optimal Network of Wearable Inertial Measurement Units , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Héctor Pomares,et al.  Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition , 2014, Sensors.

[16]  Paul Lukowicz,et al.  Sensor Placement Variations in Wearable Activity Recognition , 2014, IEEE Pervasive Computing.

[17]  A. M. Sabatini,et al.  A novel functional calibration method for real-time elbow joint angles estimation with magnetic-inertial sensors. , 2017, Journal of biomechanics.

[18]  Mark de Zee,et al.  Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture , 2016, Sensors.

[19]  Bertram Taetz,et al.  On Inertial Body Tracking in the Presence of Model Calibration Errors , 2016, Sensors.

[20]  Bertram Taetz,et al.  IMU-to-Segment Assignment and Orientation Alignment for the Lower Body Using Deep Learning , 2018, Sensors.

[21]  Taeyoung Kim,et al.  Characterising and minimising sources of error in inertial body sensor networks , 2013, Int. J. Auton. Adapt. Commun. Syst..