Model-Based Motion Filtering for Improving Arm Gesture Recognition Performance
NAVAL RESEARCH LAB WASHINGTON DC ADVANCED INFORMATION TECHNOLOGY BRANCH
Pagination or Media Count:
We describe a model-based motion filtering process that when applied to human arm motion data leads to improved arm gesture recognition. By arm gestures, we mean movements of the arm and positional placement of the hand that may or may not have any meaningful intent. Arm movements or gestures can be viewed as responses to muscle actuations that are guided by responses of the nervous system. Our method makes strides towards capturing this underlying knowledge of human performance by integrating a model for the arm based on dynamics and containing a control system. We hypothesize that by embedding the human performance knowledge into the processing of arm movements, it will lead to better recognition performance. We present details for the design of our filter, our analysis of the filter from both expert-user and multiple-user pilot studies. Our results show that the filter has a positive impact on the recognition performance for arm gestures.
- Computer Programming and Software