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.
KeywordsInertial sensors Quaternion-based calibration Human motion analysis Joint angular kinematics
This work was partially funded by the Ecuadorian Consortium for Advanced Internet Development (CEDIA) through the CEPRA projects. Specifically, under grants CEPRA-X-2016 project; “Tele-rehabilitation platform for elderly with dementia disorders, based on emerging technologies”. [Grant number: X-2016-02].
- 1.Díaz, I., Gil, J.J., Sánchez, E.: Lower-limb robotic rehabilitation: literature review and challenges. J. Robot. 2011(i), 1–11 (2011)Google Scholar
- 4.Makino, Y., Tsujiuchi, N., Ito, A., Koizumi, T., Nakamura, S., Matsuda, Y., Tsuchiya, Y., Hayashi, Y.: Quantitative evaluation of unrestrained human gait on change in walking velocity. In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2014, pp. 2521–2524 (2014)Google Scholar
- 5.Roetenberg, D.: Xsens MVN : Full 6DOF Human Motion Tracking Xsens MVN : Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors. Technical report, XSENS TECHNOLOGIES, January 2009Google Scholar
- 6.Jakob, C., Kugler, P., Hebenstreit, F., Reinfelder, S., Jensen, U., Schuldhaus, D., Lochmann, M., Eskofier, B.: Estimation of the knee flexion-extension angle during dynamic sport motions using body-worn inertial sensors. In: Proceedings of the 8th International Conference on Body Area Networks (2013)Google Scholar
- 9.Narváez, F., Marín-Castrillón, D.M., Cuenca, M.C., Latta, M.A.: Development and implementation of technologies for physical telerehabilitation in Latin America : a systematic review of literature, programs and projects Desarrollo e implementación de tecnologías. TecnoLógicas 20(40), 155–176 (2017)CrossRefGoogle Scholar
- 12.Narvaez, F., Fernando, A., Luna, C., Merchan, C., Cuenca, M.C., Diaz, G.: Kushkalla: a web-based platform to improve functional movement rehabilitation. In: Valencia-García, R., Lagos-Ortiz, K., Alcaraz-Mármol, G., Del Cioppo, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds.) Technologies and Innovation. CCIS, vol. 749, pp. 194–208. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67283-0_15CrossRefGoogle Scholar
- 16.Sun, T., Liu, Q., Li, W., Lu, Z., Chen, H., Chen, P., Lu, Z.: Hip, knee and ankle motion angle detection based on inertial sensor. In: Proceedings of the IEEE International Conference on Information and Automation, pp. 1612–1617, August 2016Google Scholar
- 17.Wang, Y., Xu, J., Wu, X., Pottie, G., Kaiser, W.: A simple calibration for upper limb motion tracking and reconstruction or reconstruction upper limb motion tracking and reconstruction. In: Conference of Proceedings of the IEEE Engineering in Medicine and Biology Society, pp. 5868–5871 (2014)Google Scholar
- 23.Wu, G., Siegler, S., Allard, P., Kirtley, C., Leardini, A., Rosenbaum, D., Whittle, M., D’Lima, D.D., Cristofolini, L., Witte, H., Schmid, O., Stokes, I.: ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion–part I: ankle, hip, and spine. J. Biomech. 35(4), 543–548 (2002)CrossRefGoogle Scholar