Activity recognition using biomechanical model based pose estimation

Authors:
Attila Reiss;Gustaf Hendeby;Gabriele Bleser;Didier Stricker
Affiliations:
German Research Center for Artificial Intelligence, Department of Augmented Vision, Kaiserslautern, Germany;German Research Center for Artificial Intelligence, Department of Augmented Vision, Kaiserslautern, Germany;German Research Center for Artificial Intelligence, Department of Augmented Vision, Kaiserslautern, Germany;German Research Center for Artificial Intelligence, Department of Augmented Vision, Kaiserslautern, Germany
Venue:
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
Year:
2010

Citing 11
Cited 4

Autocalibration of an Electronic Compass for Augmented Reality

ISMAR '05 Proceedings of the 4th IEEE/ACM International Symposium on Mixed and Augmented Reality
Analyzing features for activity recognition

Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Development of a Tiny Orientation Estimation Device to Operate under Motion and Magnetic Disturbance

International Journal of Robotics Research
Practical motion capture in everyday surroundings

ACM SIGGRAPH 2007 papers
Using physical activity for user behavior analysis

Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Multi Activity Recognition Based on Bodymodel-Derived Primitives

LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
An Analysis of Sensor-Oriented vs. Model-Based Activity Recognition

ISWC '09 Proceedings of the 2009 International Symposium on Wearable Computers
Feature selection and activity recognition from wearable sensors

UCS'06 Proceedings of the Third international conference on Ubiquitous Computing Systems
Activity classification using realistic data from wearable sensors

IEEE Transactions on Information Technology in Biomedicine
Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring

IEEE Transactions on Information Technology in Biomedicine
Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions

IEEE Transactions on Information Technology in Biomedicine

Qualitative activity recognition of weight lifting exercises

Proceedings of the 4th Augmented Human International Conference
Egocentric activity monitoring and recovery

ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
A generic approach to inertial tracking of arbitrary kinematic chains

BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
A personalized exercise trainer for the elderly

Journal of Ambient Intelligence and Smart Environments

Quantified Score

Hi-index	0.03

Visualization

Abstract

In this paper, a novel activity recognition method based on signal-oriented and model-based features is presented. The model-based features are calculated from shoulder and elbow joint angles and torso orientation, provided by upper-body pose estimation based on a biomechanical body model. The recognition performance of signal-oriented and model-based features is compared within this paper, and the potential of improving recognition accuracy by combining the two approaches is proved: the accuracy increased by 4-6% for certain activities when adding model-based features to the signal-oriented classifier. The presented activity recognition techniques are used for recognizing 9 everyday and fitness activities, and thus can be applied for e.g., fitness applications or 'in vivo' monitoring of patients.