Motion Analysis in Clinical Practice Using Ambulatory Accelerometry
CAPTECH '98 Proceedings of the International Workshop on Modelling and Motion Capture Techniques for Virtual Environments
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Discriminative Density Propagation for 3D Human Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Performance animation from low-dimensional control signals
ACM SIGGRAPH 2005 Papers
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Relevant Feature Selection for Human Pose Estimation and Localization in Cluttered Images
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Gaussian process latent variable models for human pose estimation
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
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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.