Validity of inertial sensor based 3D joint kinematics of static and dynamic sport and physiotherapy specific movements

3D joint kinematics can provide important information about the quality of movements. Optical motion capture systems (OMC) are considered the gold standard in motion analysis. However, in recent years, inertial measurement units (IMU) have become a promising alternative. The aim of this study was to validate IMU-based 3D joint kinematics of the lower extremities during different movements. Twenty-eight healthy subjects participated in this study. They performed bilateral squats (SQ), single-leg squats (SLS) and countermovement jumps (CMJ). The IMU kinematics was calculated using a recently-described sensor-fusion algorithm. A marker based OMC system served as a reference. Only the technical error based on algorithm performance was considered, incorporating OMC data for the calibration, initialization, and a biomechanical model. To evaluate the validity of IMU-based 3D joint kinematics, root mean squared error (RMSE), range of motion error (ROME), Bland-Altman (BA) analysis as well as the coefficient of multiple correlation (CMC) were calculated. The evaluation was twofold. First, the IMU data was compared to OMC data based on marker clusters; and, second based on skin markers attached to anatomical landmarks. The first evaluation revealed means for RMSE and ROME for all joints and tasks below 3°. The more dynamic task, CMJ, revealed error measures approximately 1° higher than the remaining tasks. Mean CMC values ranged from 0.77 to 1 over all joint angles and all tasks. The second evaluation showed an increase in the RMSE of 2.28°– 2.58° on average for all joints and tasks. Hip flexion revealed the highest average RMSE in all tasks (4.87°– 8.27°). The present study revealed a valid IMU-based approach for the measurement of 3D joint kinematics in functional movements of varying demands. The high validity of the results encourages further development and the extension of the present approach into clinical settings.

[1]  K Manal,et al.  Knee moment profiles during walking: errors due to soft tissue movement of the shank and the influence of the reference coordinate system. , 2002, Gait & posture.

[2]  Bertram Taetz,et al.  Towards an Inertial Sensor-Based Wearable Feedback System for Patients after Total Hip Arthroplasty: Validity and Applicability for Gait Classification with Gait Kinematics-Based Features , 2019, Sensors.

[3]  A. Cappello,et al.  A new formulation of the coefficient of multiple correlation to assess the similarity of waveforms measured synchronously by different motion analysis protocols. , 2010, Gait & posture.

[4]  Bertram Taetz,et al.  Real-time inertial lower body kinematics and ground contact estimation at anatomical foot points for agile human locomotion , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[5]  R Dumas,et al.  Soft tissue artifact compensation by linear 3D interpolation and approximation methods. , 2009, Journal of biomechanics.

[6]  Qingguo Li,et al.  Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics , 2013, Physiological measurement.

[7]  Lee Burton,et al.  Functional movement screening: the use of fundamental movements as an assessment of function - part 1. , 2014, International journal of sports physical therapy.

[8]  Christian Larue,et al.  Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis , 2017, Medical & Biological Engineering & Computing.

[9]  Kamiar Aminian,et al.  Joint Inertial Sensor Orientation Drift Reduction for Highly Dynamic Movements , 2018, IEEE Journal of Biomedical and Health Informatics.

[10]  Bertram Taetz,et al.  Validity, Test-Retest Reliability and Long-Term Stability of Magnetometer Free Inertial Sensor Based 3D Joint Kinematics , 2018, Sensors.

[11]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal Chiropractic Medicine.

[12]  Julius Hannink,et al.  Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters , 2017, Sensors.

[13]  K. Manal,et al.  Comparison of surface mounted markers and attachment methods in estimating tibial rotations during walking: an in vivo study. , 2000, Gait & posture.

[14]  D. Altman,et al.  Measuring agreement in method comparison studies , 1999, Statistical methods in medical research.

[15]  A. Leardini,et al.  A new anatomically based protocol for gait analysis in children. , 2007, Gait & posture.

[16]  Angelo M. Sabatini,et al.  Estimating Orientation Using Magnetic and Inertial Sensors and Different Sensor Fusion Approaches: Accuracy Assessment in Manual and Locomotion Tasks , 2014, Sensors.

[17]  R. Gathercole,et al.  Alternative countermovement-jump analysis to quantify acute neuromuscular fatigue. , 2015, International journal of sports physiology and performance.

[18]  M. Izquierdo,et al.  Vertical jumping biomechanical evaluation through the use of an inertial sensor-based technology , 2016, Journal of sports sciences.

[19]  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.

[20]  Christian Larue,et al.  Accuracy and repeatability of single-pose calibration of inertial measurement units for whole-body motion analysis. , 2017, Gait & posture.

[21]  M. Ritter,et al.  The Importance of Range of Motion after Total Hip Arthroplasty , 2007, Clinical orthopaedics and related research.

[22]  K Bo Foreman,et al.  Soft tissue artifact causes significant errors in the calculation of joint angles and range of motion at the hip. , 2017, Gait & posture.

[23]  Ruchika Tadakala,et al.  Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement , 2020, Sensors.

[24]  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.

[25]  Chris Bishop,et al.  Next-generation low-cost motion capture systems can provide comparable spatial accuracy to high-end systems. , 2013, Journal of applied biomechanics.

[26]  Kate Button,et al.  Inertial Measurement Units for Clinical Movement Analysis: Reliability and Concurrent Validity , 2018, Sensors.

[27]  Kamiar Aminian,et al.  Validation of functional calibration and strap-down joint drift correction for computing 3D joint angles of knee, hip, and trunk in alpine skiing , 2017, PloS one.

[28]  Christopher P Carty,et al.  Kinematic predictors of single-leg squat performance: a comparison of experienced physiotherapists and student physiotherapists , 2012, BMC Musculoskeletal Disorders.

[29]  Corina Nüesch,et al.  Measuring joint kinematics of treadmill walking and running: Comparison between an inertial sensor based system and a camera-based system. , 2017, Journal of biomechanics.

[30]  H. Menzel,et al.  Analysis of Lower Limb Asymmetries by Isokinetic and Vertical Jump Tests in Soccer Players , 2013, Journal of strength and conditioning research.

[31]  S. Piva,et al.  Evaluating Eccentric Hip Torque and Trunk Endurance as Mediators of Changes in Lower Limb and Trunk Kinematics in Response to Functional Stabilization Training in Women With Patellofemoral Pain , 2015, The American journal of sports medicine.

[32]  D. Altman,et al.  Agreement Between Methods of Measurement with Multiple Observations Per Individual , 2007, Journal of biopharmaceutical statistics.

[33]  April L. McPherson,et al.  Sagittal plane kinematic differences between dominant and non-dominant legs in unilateral and bilateral jump landings. , 2016, Physical therapy in sport : official journal of the Association of Chartered Physiotherapists in Sports Medicine.

[34]  B. Galna,et al.  Quantification of soft tissue artifact in lower limb human motion analysis: a systematic review. , 2010, Gait & posture.

[35]  I Jonkers,et al.  Discriminant validity of 3D joint kinematics and centre of mass displacement measured by inertial sensor technology during the unipodal stance task , 2020, PloS one.

[36]  A. Cappozzo,et al.  Human movement analysis using stereophotogrammetry. Part 3. Soft tissue artifact assessment and compensation. , 2005, Gait & posture.

[37]  M Damsgaard,et al.  Surface marker cluster translation, rotation, scaling and deformation: Their contribution to soft tissue artefact and impact on knee joint kinematics. , 2015, Journal of biomechanics.

[38]  Valentina Camomilla,et al.  Countermovement jump performance assessment using a wearable 3D inertial measurement unit , 2011, Journal of sports sciences.

[39]  Patrick Boissy,et al.  Inertial measurement systems for segments and joints kinematics assessment: towards an understanding of the variations in sensors accuracy , 2017, BioMedical Engineering OnLine.

[40]  A. Cappozzo,et al.  Human movement analysis using stereophotogrammetry. Part 1: theoretical background. , 2005, Gait & posture.

[41]  Richard Baker,et al.  Proximal placement of lateral thigh skin markers reduces soft tissue artefact during normal gait using the Conventional Gait Model , 2016, Computer methods in biomechanics and biomedical engineering.

[42]  Noel C. Perkins,et al.  Quantifying the effects of load carriage and fatigue under load on sacral kinematics during countermovement vertical jump with IMU-based method , 2016 .

[43]  Minking Eie,et al.  Generalizations of Euler decomposition and their applications , 2013 .

[44]  Alberto Leardini,et al.  Soft tissue artifact compensation in knee kinematics by double anatomical landmark calibration: performance of a novel method during selected motor tasks , 2005, IEEE Transactions on Biomedical Engineering.

[45]  B. Wolf,et al.  Development of a Non-Invasive Blink Reflexometer , 2017, IEEE Journal of Translational Engineering in Health and Medicine.

[46]  S. Wearing,et al.  Can measures of limb loading and dynamic stability during the squat maneuver provide an index of early functional recovery after unilateral total hip arthroplasty? , 2014, Archives of physical medicine and rehabilitation.

[47]  D. Giavarina Understanding Bland Altman analysis , 2015, Biochemia medica.

[48]  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.

[49]  N. Elvin,et al.  Correlation between ground reaction force and tibial acceleration in vertical jumping. , 2007, Journal of applied biomechanics.

[50]  Kamiar Aminian,et al.  Soft tissue artifact distribution on lower limbs during treadmill gait: Influence of skin markers' location on cluster design. , 2015, Journal of biomechanics.

[51]  Gentiane Venture,et al.  Joint kinematics estimation using a multi-body kinematics optimisation and an extended Kalman filter, and embedding a soft tissue artefact model. , 2017, Journal of biomechanics.

[52]  A Forner-Cordero,et al.  Study of the motion artefacts of skin-mounted inertial sensors under different attachment conditions , 2008, Physiological measurement.

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

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