Abstract
Inertial measurement units (IMUs) have been utilized as motion capture (MoCap) devices in computer graphics, biomechanics, and rehabilitation. Typically, full body motions are estimated from the orientation and/or acceleration data of 13–17 IMUs attached to the body segments of experimental subjects. However, attaching numerous IMUs is quite intrusive and sometimes restricts the subjects’ motions. Recent advances in machine learning technologies have enabled full body motion estimation from a sparse set of IMUs (6–7 units). The present study compares the motion estimation accuracies of a system with a full set of IMUs (called full IMU MoCap) and a system with a sparse set of IMUs (called sparse IMU MoCap). Three male subjects performed three walking trials with different stride lengths (normal, short, and long), and their full body motions were estimated by each MoCap. Finally, the gait-related factors were calculated from each set of motion estimation results, and compared with the ground-truth data obtained by an optical marker-based MoCap. Although the sparse IMU MoCap achieved a lower overall accuracy than the full IMU MoCap, it can potentially evaluate the relative changes in the functionality of the locomotor during walking.
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References
Roetenberg, D., Luinge H., Slycke, P.: Xsens MVN full 6DOF human motion tracking using miniature inertial sensors, Xsens Motion Technologies BV. Technical Report, 1–10 (2009)
Teufl, W., Miezal, M., Taetzl, B., Fröhlich M., Bleser G.: Validity, test-retest reliability and long-term stability of magnetometer free inertial sensor based 3D joint kinematics. J. Sens. 18 (7), 1980:1–1980:22 (2018)
Mousas, C.: Full body locomotion reconstruction of virtual characters using a single inertial measurement unit. J. Sens. 17(11), 2589 (2017)
Huang, Y., Kaufmann M., Aksan, E. Black, M. J., Hilliges, O., Pons-Moll, G.: Deep Inertial Poser: learning to reconstruct human pose from sparse inertial measurements in real time, ACM Trans. Graph. 37 (6), 185:1–185:15 (2018)
Toda, H., Maruyama, T., Tada, M.: Validity of the kinematic profiles obtained by a real-time motion capture system using inertial measurement units during timed up and go test. In: 13th World Congress of International Society of Physical and Rehabilitation Medicine (2019)
Guo, L., Xiong, S.: Accuracy of base of support using an inertial sensor based motion capture system, J. Sens. 17 (9), 2091:1–2091:24 (2007)
Maruyama, T., Tada, M., Toda, H.: Riding motion capture system using inertial measurement units with contact constraints. J. Autom. Technol. 13(5), 506–516 (2019)
Maruyama, T., Toda, H., Tada, M.: Inertial measurement unit to segment calibration based on constrained pose estimation. In: Proceedings of SICE Annual Conference 2019, 719-722 (2019)
Endo, Y., Tada, M., Mochimaru, M.: Dhaiba: development of virtual ergonomic assessment system with human models. In: Proceedings of Digital Human Modeling 2014, #58 (2014)
Endo, Y., Tada, M., Mochimaru, M.: Estimation of arbitrary human models from anthropometric dimensions. In: Proceedings of the 17th International Conference on Human-Computer Interaction, pp. 3–14 (2015)
AIST Japanese Body Size Database. Available via https://unit.aist.go.jp/hiri/dhrg/ja/dhdb/91-92/index.html (In Japanese). Cited 30 Jan 2020
Maruyama, T., Tada, M., Sawatome, A., Endo, Y. Constraint-based real-time full body motion-capture using inertial measurement units. In: Proceedings of 2018 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4298–4303 (2018)
Trumble, M., Gilbert, A., Malleson, C., Hilton, A., Collomosse, J.: Total capture: 3d human pose estimation fusing video and inertial. In: Proceedings of 28th British Machine Vision Conference, pp. 1–13 (2017)
MPI DIP. Available via https://dip.is.tuebingen.mpg.de/. Cited 30 Jan 2020
Toda, H., Tada, M., Maruyama, T., Kurita, Y.: Effect of contraction parameters on swing support during walking using wireless pneumatic artificial muscle driver: a preliminary study. In: Proceedings of SICE Annual Conference 2019, pp. 727-732 (2019)
Kanzaki, T., Sawatome A., Tsuichihara S., Takemura H., Tada M.: Increasing exercise intensity during walking by sound intervention using real-time gait event detection. In: Proceediings of the 2019 IEEE/SICE International Symposium on System Integration, pp 681–686 (2020)
Vicon. Available in https://www.vicon.com/. Cited 30 Jan 2020
Xsens MTw Awinda. Available in https://www.xsens.com/products/mtw-awinda/. Cited 30 Jan 2020
Iosa, M., Cereatti, A., Merlo, A., Campanini, I., Paolucci, S., Cappozzo, A.: Assessment of waveform similarity in clinical gait data: the linear fit method. J. Biomed. Biotechnol. 2014, 214156:1–214156:7 (2014)
Ojeda, L. V., Zaferiou, A. M., Cain, S. M.: Estimating Stair running performance using inertial sensors. J. Sens. 17 (11) 2647:1–2647:1 (2017)
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This work was supported by JSPS KAKENHI Grant Number 19K15255.
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Maruyama, T., Toda, H., Kanoga, S., Tada, M., Endo, Y. (2021). Accuracy Evaluation of Human Gait Estimation by a Sparse Set of Inertial Measurement Units. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-15-8944-7_4
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DOI: https://doi.org/10.1007/978-981-15-8944-7_4
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