Abstract
Body pose estimation is crucial for many human motion-related applications. Stable and low-cost methods for body pose estimation are highly desirable for daily life usage. In this paper, we propose a sensor fusion method for upper body pose estimation. A monocular camera and several IMU-marker sensor modules are integrated to achieve a visual-inertial sensor system. ArUco markers are attached to the IMUs to remove the usage of magnetometers. The raw IMU data are firstly corrected with pre-calibrated intrinsic parameters and then fused with the marker poses detected from the images via an extended Kalman filter. The driftless sensor orientation and marker trajectory are then imported into the OpenSim software to compute body states using the inverse kinematics approach. Experiments were conducted on human subjects to validate the effectiveness of the method. Movements used in the Fugl-Meyer assessment procedure are adopted during the experiments. The estimation results are compared with the joint states from the optical motion capture system showing good accuracy. The proposed method shows the potential to be applied in clinical assessment to reduce the efforts of professional physicians.
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References
Dean, C., Mackey, F.: Motor assessment scale scores as a measure of rehabilitation outcome following stroke. Aust. J. Physiother. 38, 31–35 (1992)
Domínguez-Téllez, P., Moral-Muñoz, J.A., Salazar, A., Casado-Fernández, E., Lucena-Antón, D.: Game-based virtual reality interventions to improve upper limb motor function and quality of life after stroke: systematic review and meta-analysis. Games Health J. 9, 1–10 (2020)
Menolotto, M., Komaris, D.S., Tedesco, S., O'Flynn, B., Walsh, M.: Motion capture technology in industrial applications: a systematic review. Sensors 20 (2020)
Eichelberger, P., et al.: Analysis of accuracy in optical motion capture - a protocol for laboratory setup evaluation. J. Biomech. 49, 2085–2088 (2016)
Li, T., Wang, L., Yi, J., Li, Q., Liu, T.: Reconstructing walking dynamics from two shank-mounted inertial measurement units. IEEE/ASME Trans. Mechatron. 26, 3040–3050 (2021)
Nazarahari, M., Rouhani, H.: 40 years of sensor fusion for orientation tracking via magnetic and inertial measurement units: methods, lessons learned, and future challenges. Inf. Fusion 68, 67–84 (2021)
Picerno, P.: 25 years of lower limb joint kinematics by using inertial and magnetic sensors: a review of methodological approaches. Gait Posture 51, 239–246 (2017)
Picerno, P., et al.: Upper limb joint kinematics using wearable magnetic and inertial measurement units: an anatomical calibration procedure based on bony landmark identification. Sci. Rep. 9, 1–10 (2019)
Li, T., Wu, X., Dong, H., Yu, H.: Estimation of upper limb kinematics with a magnetometer-free egocentric visual-inertial system. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1668–1674 (2022)
Barre, A., Thiran, J.P., Jolles, B.M., Theumann, N., Aminian, K.: Soft tissue artifact assessment during treadmill walking in subjects with total knee arthroplasty. IEEE Trans. Biomed. Eng. 60, 3131–3140 (2013)
Fiorentino, N.M., Atkins, P.R., Kutschke, M.J., Goebel, J.M., Foreman, K.B., Anderson, A.E.: Soft tissue artifact causes significant errors in the calculation of joint angles and range of motion at the hip. Gait Posture 55, 184–190 (2017)
Li, T., Yu, H.: Upper body pose estimation using a visual-inertial sensor system with automatic sensor-to-segment calibration. IEEE Sens. J., 1–11 (2023)
Tedaldi, D., Pretto, A., Menegatti, E.: A robust and easy to implement method for IMU calibration without external equipments. In: IEEE International Conference on Robotics and Automation, pp. 3042–3049. IEEE (2014)
Mallat, R., Bonnet, V., Khalil, M.A., Mohammed, S.: Upper limbs kinematics estimation using affordable visual-inertial sensors. IEEE Trans. Autom. Sci. Eng. 19, 1–11 (2022)
Gladstone, D.J., Danells, C.J., Black, S.E.: The Fugl-Meyer assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabil. Neural Repair 16, 232–240 (2002)
Acknowledgment
The authors thank the support from (1) the Base and Talent Special Project of Guangxi Science and Technology Plan Project (Gui Ke AD23026285); (2) the Basic Ability Promotion Project for Yong Teachers in Guangxi (2023KY0013).
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Li, T., Wang, J., Chen, Y., Dong, T. (2023). Visual–Inertial Sensor Fusion and OpenSim Based Body Pose Estimation. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14268. Springer, Singapore. https://doi.org/10.1007/978-981-99-6486-4_24
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DOI: https://doi.org/10.1007/978-981-99-6486-4_24
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