A new approach to enable gesture recognition in continuous data streams

Gesture recognition has great potential for mobile and wearable computing. Most papers in this area focus on classifying different gestures, but do not evaluate the distinctiveness of gestures in continuous recordings of gestures in daily life. This paper presents a new approach for the important and challenging problem of gesture recognition in continuous data streams. We use turning points of arm movements to identify segments of interest in the continuous data stream. The recognition algorithm considers both the direction of movements between turning points and the shape of the turning points for classification. Using the new method, seven gestures of different complexity are evaluated against a realistic background class of daily gestures in five different scenarios.

[1]  Jani Mäntyjärvi,et al.  Accelerometer-based gesture control for a design environment , 2006, Personal and Ubiquitous Computing.

[2]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[3]  Bernt Schiele,et al.  Toward Recognition of Short and Non-repetitive Activities from Wearable Sensors , 2007, AmI.

[4]  Tapio Seppänen,et al.  Hand gesture recognition of a mobile device user , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[5]  Alex Pentland,et al.  Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Jani Mäntyjärvi,et al.  Enabling fast and effortless customisation in accelerometer based gesture interaction , 2004, MUM '04.

[7]  Kristofer S. J. Pister,et al.  Acceleration sensing glove (ASG) , 1999, Digest of Papers. Third International Symposium on Wearable Computers.

[8]  Bernt Schiele,et al.  Browsing patient records during ward rounds with a body worn gyroscope , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[9]  Jani Mäntyjärvi,et al.  User Independent Gesture Interaction for Small Handheld Devices , 2006, Int. J. Pattern Recognit. Artif. Intell..

[10]  Timo Pylvänäinen,et al.  Accelerometer Based Gesture Recognition Using Continuous HMMs , 2005, IbPRIA.