PublicationDetail

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

L. Schwarz, D. Mateus, N. Navab
Recognizing Multiple Human Activities and Tracking Full-Body Pose in Unconstrained Environments
Pattern Recognition, Volume 45 (2012), p. 11-23 (bib)

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 dataset 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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.



Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif