Online hand gesture recognition using neural network based segmentation

Authors:
Chun Zhu;Weihua Sheng
Affiliations:
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK;School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 3
Cited 0

Neural network design

Neural network design
An HMM-Based Threshold Model Approach for Gesture Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Gesture spotting with body-worn inertial sensors to detect user activities

Pattern Recognition

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Abstract

In this paper, we propose an online hand gesture recognition algorithm for a robot assisted living system. A neural network-based gesture spotting method is combined with the hierarchical hidden Markov model (HHMM) to recognize hand gestures. In the segmentation module, the neural network is used to determine whether the HHMM-based recognition module should be applied. In the recognition module, Bayesian filtering is applied to update the results considering the context constraints. We implemented the algorithm using an inertial sensor worn on a finger of the human subject. The obtained results prove the accuracy and effectiveness of our algorithm.