An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learing Compliant Motions by Task-Demonstration in Virtual Environments
The 4th International Symposium on Experimental Robotics IV
Exploring Grahpical Feedback in a Demonstrational Visual Shell
EWHCI '94 Selected papers from the 4th International Conference on Human-Computer Interaction
X-AiD: a shell for building highly interactive and adaptive user interfaces
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
A flexible approach to gesture recognition and interaction in X3D
Proceedings of the 17th International Conference on 3D Web Technology
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In recent years, the demand for efficient systems which can capture and learn human skills has become increasingly important. In this paper an approach for acquiring and recognizing human assembly skills is presented. The underlying workflows of assembly skills are captured by using a simple multi-sensor data glove and camera tracking. To avoid the processing of redundant information, at first the relevant tasks of a workflow are identified by analyzing measuring information of the multi-sensor capturing system. Thus, only relevant data is comprised in the representation of a workflow. Unlike common approaches a workflow is modeled as entire unit using a continuous Hidden Markov Model (HMM). The recognition process of input patterns is based on an adaptive threshold model that can identify known workflow patterns and non-meaningful input patterns as well.