Abstract
In this paper, a method is presented that allows reconstructing the full-body pose of a person in real-time, based on the limited input from a few wearable inertial sensors. Our method uses Gaussian Process Regression to learn the person-specific functional relationship between the sensor measurements and full-body pose. We generate training data by recording sample movements for different activities simultaneously using inertial sensors and an optical motion capture system. Since our approach is discriminative, pose prediction from sensor data is efficient and does not require manual initialization or iterative optimization in pose space. We also propose a SVM-based scheme to classify the activities based on inertial sensor data. An evaluation is performed on a dataset of movements, such as walking or golfing, performed by different actors. Our method is capable of reconstructing the full-body pose from as little as four inertial sensors with an average angular error of 4-6 degrees per joint, as shown in our experiments.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aminian, K., Andres, E.D., Rezakhanlou, K., Fritsch, C., Robert, P.: Motion analysis in clinical practice using ambulatory accelerometry. In: Magnenat-Thalmann, N., Thalmann, D. (eds.) CAPTECH 1998. LNCS (LNAI), vol. 1537, pp. 1–11. Springer, Heidelberg (1998)
Cunha, J., Li, Z., Fernandes, J., Feddersen, B., Noachtar, S.: Movement quantification during epileptic seizures: a new technical contribution to the evaluation of the seizure semiology. In: International Conference of the IEEE EMBS (2003)
Jovanov, E., Milenkovic, A., Otto, C., Groen, P.D.: A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. Journal of NeuroEngineering and Rehabilitation 2(1), 6 (2005)
Najafi, B., Aminian, K., Paraschiv-Ionescu, A.: Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Transactions on Biomedical Engineering (January 2003)
Luinge, H., Veltink, P., Baten, C.: Ambulatory measurement of arm orientation. Journal of Biomechanics 40(1), 78–85 (2007)
Roetenberg, D., Slycke, P., Veltink, P.: Ambulatory position and orientation tracking fusing magnetic and inertial sensing. IEEE Transactions on Biomedical Engineering 54(4), 883–890 (2007)
Zhou, H., Stone, T., Hu, H., Harris, N.: Use of multiple wearable inertial sensors in upper limb motion tracking. Medical Engineering and Physics 30, 123–133 (2008)
Grochow, K., Martin, S., Hertzmann, A., Popović, Z.: Style-based inverse kinematics. ACM Transactions on Graphics 23(3), 522–531 (2004)
Gupta, A., Chen, T., Chen, F., Kimber, D., Davis, L.: Context and observation driven latent variable model for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)
Shon, A., Grochow, K., Hertzmann, A., Rao, R.: Learning shared latent structure for image synthesis and robotic imitation. Neural Information Processing Systems (NIPS) 18, 1233 (2006)
Urtasun, R., Fleet, D., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: IEEE International Conference on Computer Vision, ICCV (2005)
Agarwal, A., Triggs, B., Montbonnot, F.: Recovering 3d human pose from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 28(1), 44–58 (2006)
Fossati, A., Salzmann, M., Fua, P.: Observable subspaces for 3d human motion recovery. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), March 2009, pp. 1–8 (2009)
Urtasun, R., Darrell, T.: Sparse probabilistic regression for activity-independent human pose inference. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (January 2008)
Zhao, X., Ning, H., Liu, Y., Huang, T.: Discriminative estimation of 3d human pose using gaussian processes. In: International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)
Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Discriminative density propagation for 3d human motion estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2005)
Sun, Y., Bray, M., Thayananthan, A., Yuan, B., Torr, P.: Regression-based human motion capture from voxel data. In: British Machine Vision Conference, BMVC (2006)
Okada, R., Soatto, S.: Relevant feature selection for human pose estimation and localization in cluttered images. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 434–445. Springer, Heidelberg (2008)
Chai, J., Hodgins, J.: Performance animation from low-dimensional control signals. In: International Conference on Computer Graphics and Interactive Techniques, pp. 686–696 (2005)
Zhu, R., Zhou, Z.: A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. IEEE Transactions on Neural Systems and Rehabilitation Engineering 12(2), 295–302 (2004)
Rasmussen, C., Williams, C.: Gaussian processes for machine learning (2006)
Ek, C., Torr, P., Lawrence, N.: Gaussian process latent variable models for human pose estimation. Machine Learning for Multimodal Interaction, 132–143 (2008)
Lawrence, N.: Gaussian process latent variable models for visualisation of high dimensional data. Neural Information Processing Systems, NIPS (2004)
Lawrence, N.: Gaussian process library (2009), http://www.cs.man.ac.uk/neill/gplvm/
Abe, S.: Support Vector Machines for Pattern Classification. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schwarz, L.A., Mateus, D., Navab, N. (2009). Discriminative Human Full-Body Pose Estimation from Wearable Inertial Sensor Data. In: Magnenat-Thalmann, N. (eds) Modelling the Physiological Human. 3DPH 2009. Lecture Notes in Computer Science, vol 5903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10470-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-10470-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10468-8
Online ISBN: 978-3-642-10470-1
eBook Packages: Computer ScienceComputer Science (R0)