ProjectSensorsHumanMotion

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

THIS WEBPAGE IS DEPRECATED - please visit our new website

Activity Recognition and Pose Estimation using Wearable Inertial Sensors

Activity Recognition and Pose Estimation using Wearable Inertial Sensors

Scientific Director: Nassir Navab

Contact Person(s): Loren Schwarz

Keywords: Computer Vision

Abstract

We propose a method for human full-body pose tracking from measurements of wearable inertial sensors. Since the data provided by such sensors is sparse, noisy and often ambiguous, we use a compound prior model of feasible human poses to constrain the tracking problem. Our model consists of several low-dimensional, activity-specific motion models and an efficient, sampling-based activity switching mechanism. We restrict the search space for pose tracking by means of manifold learning. Together with the portability of wearable sensors, our method allows us to track human full-body motion in unconstrained environments. In fact, we are able to simultaneously classify the activity a person is performing and estimate the full-body pose. Experiments on movement sequences containing different activities show that our method can seamlessly detect activity switches and precisely reconstruct full-body pose from the data of only six wearable inertial sensors.

Team

Contact Person(s)

Loren Schwarz
Dr. Loren Schwarz

Working Group

Diana Mateus
Prof. Diana Mateus
Loren Schwarz
Dr. Loren Schwarz

Location



Technische Universität München
Institut für Informatik / I16
Boltzmannstr. 3
85748 Garching bei München

Tel.: +49 89 289-17058
Fax: +49 89 289-17059
Visit our lab at Garching.



internal project page

Please contact Loren Schwarz for available student projects within this research project.

Video

Dataset

A dataset of synchronized motion capture and inertial orientation sensor data will be released for download soon. The dataset contains recordings of 9 actors performing 11 activities, each activity in 5 repetitions.



Edit | Attach | Refresh | Diffs | More | Revision r1.5 - 15 Jun 2011 - 11:44 - LorenSchwarz

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