A Quaternion-Based Method to IMU-to-Body Alignment for Gait Analysis

Human gait analysis based on inertial measurement units (IMUs) is still considered a challenging task. This is because the accurate capture of human body movements depends on an initial sensor-to-body calibration and alignment process. In this paper, a novel sensor-to-body alignment method based on sequences of quaternions is presented, which allows to accurately estimate the joint angles from the hip, knee and ankle of the lower limbs. The proposed method involves two main stages, a sensors calibration and an alignment process for the body segments, respectively. For doing that, two different sequences of rotation based on Euler angle-axis factors are developed. The first rotational sequence is used to calibrate sensor’s frame under a new general body frame by estimating the initial orientation based on its quaternion information. Then, a correction process is applied by factorizing the captured quaternions. Once the general body frame is defined, a second rotational sequence is implemented, which aligns each sensor frame to body frames, allowing to define the anatomic frames for obtaining clinical measurements of the joint angles. The proposed method was two-fold validated using both strategies, a goniometer-based measure system and a camera-based motion system, respectively. The obtained results demonstrate that the estimated joint angles are equal to the expected values and consistent with values obtained by the strategies widely used in real clinical scenarios, the goniometers and optical motion system. Therefore, the proposed method could be used in clinical applications and motion analysis of impaired persons.

[1]  Begonya Garcia-Zapirain,et al.  Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications , 2014, Sensors.

[2]  Annemarie Laudanski,et al.  Measurement of lower limb joint kinematics using inertial sensors during stair ascent and descent in healthy older adults and stroke survivors. , 2013, Journal of healthcare engineering.

[3]  Carlos Daniel Luna,et al.  Kushkalla: A Web-Based Platform to Improve Functional Movement Rehabilitation , 2017, CITI.

[4]  Fabián Narváez,et al.  Development and implementation of technologies for physical telerehabilitation in Latin America , 2017 .

[5]  Patrick Boissy,et al.  Inertial Measures of Motion for Clinical Biomechanics: Comparative Assessment of Accuracy under Controlled Conditions - Effect of Velocity , 2013, PloS one.

[6]  Jack B. Kuipers,et al.  Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace and Virtual Reality , 2002 .

[7]  J. J. Gil,et al.  Lower-Limb Robotic Rehabilitation: Literature Review and Challenges , 2011, J. Robotics.

[8]  Cheong Boon Soh,et al.  Lower Extremity Joint Angle Tracking with Wireless Ultrasonic Sensors during a Squat Exercise , 2015, Sensors.

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

[10]  Hartmut Witte,et al.  ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion--part I: ankle, hip, and spine. International Society of Biomechanics. , 2002, Journal of biomechanics.

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

[12]  A. Illarramendi,et al.  Exercise Recognition for Kinect-based Telerehabilitation , 2014, Methods of Information in Medicine.

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

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

[15]  Gregory J. Pottie,et al.  A simple calibration for upper limb motion tracking and reconstruction , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Lihong Duan,et al.  Hip, knee and ankle motion angle detection based on inertial sensor , 2016, 2016 IEEE International Conference on Information and Automation (ICIA).

[17]  Fabián Narváez,et al.  Development and implementation of technologies for physical telerehabilitation in Latin America: a systematic review of literature, programs and projects , 2017 .

[18]  M P Kadaba,et al.  Measurement of lower extremity kinematics during level walking , 1990, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[19]  Björn Eskofier,et al.  Estimation of the Knee Flexion-Extension Angle During Dynamic Sport Motions Using Body-worn Inertial Sensors , 2013, BODYNETS.

[20]  Yuichiro Hayashi,et al.  Quantitative evaluation of unrestrained human gait on change in walking velocity , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Cheng Yang,et al.  A Depth Camera Motion Analysis Framework for Tele-rehabilitation: Motion Capture and Person-Centric Kinematics Analysis , 2016, IEEE Journal of Selected Topics in Signal Processing.

[22]  P R Cavanagh,et al.  ISB recommendations for standardization in the reporting of kinematic data. , 1995, Journal of biomechanics.

[23]  Tao Liu,et al.  The Lower Limbs Kinematics Analysis by Wearable Sensor Shoes , 2016, IEEE Sensors Journal.

[24]  Kenneth Sundaraj,et al.  Gait disorder rehabilitation using vision and non-vision based sensors: a systematic review. , 2012, Bosnian journal of basic medical sciences.