Model-Based Motion Filtering for Improving Arm Gesture Recognition Performance

摘要

We describe a model-based motion filtering process that, when applied to human arm motion data, leads to improved arm gesture recognition. Arm movements can be viewed as responses to muscle actuations that are guided by responses of the nervous system. Our motion filtering method makes strides towards capturing this structure by integrating a dynamic model with a control system for the arm. We hypothesize that embedding human performance knowledge into the processing of arm movements will lead to better recognition performance. We present details for the design of our filter, our evaluation of the filter from both expert-user and multiple-user pilot studies. Our results show that the filter has a positive impact on recognition performance for arm gestures.