Segmentation and recognition of motion capture data stream by classification
Multimedia Tools and Applications
Classification of multivariate time series using two-dimensional singular value decomposition
Knowledge-Based Systems
Classification of multivariate time series using locality preserving projections
Knowledge-Based Systems
Recognition of human grasps by time-clustering and fuzzy modeling
Robotics and Autonomous Systems
Biometrics-based identifiers for digital identity management
Proceedings of the 9th Symposium on Identity and Trust on the Internet
A brief survey on sequence classification
ACM SIGKDD Explorations Newsletter
Generalized Model-Based Human Motion Recognition with Body Partition Index Maps
Computer Graphics Forum
Asynchronism-based principal component analysis for time series data mining
Expert Systems with Applications: An International Journal
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Multi-attribute motion data can be generated in many applications/ devices, such as motion capture devices and animations. It can have dozens of attributes, thousands of rows, and even similar motions can have different durations and different speeds at corresponding parts. There are no row-to-row correspondences between data matrices of two motions. To be classified and recognized, multi-attribute motion data of different lengths are reduced to feature vectors by using the properties of singular value decomposition (SVD) of motion data. The reduced feature vectors of similar motions are close to each other, while reduced feature vectors are different from each other if their motions are different. By applying support vector machines (SVM) to the feature vectors, we efficiently classify and recognize real-world multi-attribute motion data. With our data set of more than 300 motions with different lengths and variations, SVM outperforms classification by related similarity measures, in terms of accuracy and CPU time. The performance of our approach shows its feasibility of real-time applications to real-world data.