Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Practical motion capture in everyday surroundings
ACM SIGGRAPH 2007 papers
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gesture spotting with body-worn inertial sensors to detect user activities
Pattern Recognition
Pose estimation and tracking using multivariate regression
Pattern Recognition Letters
Accurate Human Motion Capture Using an Ergonomics-Based Anthropometric Human Model
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Learning Generative Models for Multi-Activity Body Pose Estimation
International Journal of Computer Vision
Action-specific motion prior for efficient Bayesian 3D human body tracking
Pattern Recognition
International Journal of Computer Vision
Tracking human pose with multiple activity models
Pattern Recognition
3D human motion tracking based on a progressive particle filter
Pattern Recognition
Discriminative human full-body pose estimation from wearable inertial sensor data
3DPH'09 Proceedings of the 2009 international conference on Modelling the Physiological Human
A survey of human motion analysis using depth imagery
Pattern Recognition Letters
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Visual observations, such as camera images, are hard to obtain for long-term human motion analysis in unconstrained environments. In this paper, we present a method for human full-body pose tracking and activity recognition from measurements of few body-worn inertial orientation sensors. The sensors make our approach insensitive to illumination and occlusions and permit a person to move freely. Since the data provided by inertial sensors is sparse, noisy and often ambiguous, we use a generative prior model of feasible human poses and movements to constrain the tracking problem. Our model consists of several low-dimensional, activity-specific manifold embeddings that significantly restrict the search space for pose tracking. Using a particle filter, our method continuously explores multiple pose hypotheses in the embedding space. An efficient activity switching mechanism governs the distribution of particles across the activity-specific manifold embeddings. Selecting a pose hypothesis that best explains incoming sensor observations simultaneously allows us to classify the activity a person is performing and to estimate the full-body pose. We also derive an effective measure of predictive confidence that enables detecting anomalous movements. Experiments on a multi-person data set containing several activities show that our method can seamlessly detect activity switches and accurately reconstruct full-body poses from the data of only six wearable inertial sensors.