Unsupervised Discovery of Structure in Activity Data Using Multiple Eigenspaces

In this paper we propose a novel scheme for unsupervised detection of structure in activity data. Our method is based upon an algorithm that represents data in terms of multiple low-dimensional eigenspaces. We describe the algorithm and propose an extension that allows to handle multiple time scales. The validity of the approach is demonstrated on several data sets and using two types of acceleration features. Finally, we report on experiments that indicate that our approach can yield recognition rates comparable to other, supervised approaches.

[1]  Bernt Schiele,et al.  A model for human interruptability: experimental evaluation and automatic estimation from wearable sensors , 2004, Eighth International Symposium on Wearable Computers.

[2]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[3]  Alex Pentland,et al.  Action Reaction Learning: Automatic Visual Analysis and Synthesis of Interactive Behaviour , 1999, ICVS.

[4]  Alex Pentland,et al.  Sensing and modeling human networks using the sociometer , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[5]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[6]  Brian Patrick Clarkson,et al.  Life patterns : structure from wearable sensors , 2002 .

[7]  James W. Davis,et al.  The representation and recognition of human movement using temporal templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Kristof Van Laerhoven,et al.  Spine versus porcupine: a study in distributed wearable activity recognition , 2004, Eighth International Symposium on Wearable Computers.

[9]  Alex Pentland,et al.  Unsupervised clustering of ambulatory audio and video , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[10]  James W. Davis,et al.  The Representation and Recognition of Action Using Temporal Templates , 1997, CVPR 1997.

[11]  Henry A. Kautz,et al.  Inferring activities from interactions with objects , 2004, IEEE Pervasive Computing.

[12]  Yan Huang,et al.  Propagation networks for recognition of partially ordered sequential action , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[13]  Horst Bischof,et al.  Multiple eigenspaces , 2002, Pattern Recognit..

[14]  Blake Hannaford,et al.  A Hybrid Discriminative/Generative Approach for Modeling Human Activities , 2005, IJCAI.