The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-b...

This paper presents a framework for spotting and recognizing continuous human gestures. Skeleton based features are extracted from normalized human body coordinates to represent gestures. These features are then used to construct spatio-temporal template based Random Decision Forest models. Finally, predictions from different models are fused at score level to improve overall recognition perfor...

This paper presents a novel framework for the automatic recognition of Activities of Daily Living (ADLs), such as cooking, eating, dishwashing and watching TV, based on depth video processing and Hidden Conditional Random Fields (HCRFs). Depth video is provided by low-cost RGB-D sensors unobtrusively installed in the house. The user’s location, posture, as well as point cloud -based features re...

Motion features based on optical flow are very powerful in tasks such as the recognition of human actions or gestures. Usually, they are combined with gradient information to form a set of spatiotemporal features. However, humans can recognize gestures and actions and thus derive the implied motion out of static images alone. We model this associative recognition within a learned hierarchy of n...

We present a system for generation and recognition of oscillatory gestures. Inspired by gestures used in two representative human-tohuman control areas, we consider a set of oscillatory motions and refine from them a 24 gesture lexicon. Each gesture is modeled as a dynamical system with added geometric constraints to allow for real time gesture recognition using a small amount of processing tim...

I n this paper we use data collected from benchmark circuit simulations to examine the relationship between the tests which detect stuck-at faults and those which detect bridging surrogates. W e show that the coeficent of correlation between these tests approaches zero as the stuck-at fault coverage approaches 100%. A n enhanced version of the MPG-D model, which is based upon the number of dete...

Abstract Dynamic gesture recognition has become a new type of interaction to meet the needs daily interaction. It is most natural, easy operate, and intuitive, so it wide range applications. The accuracy depends on ability accurately learn short-term long-term spatiotemporal features gestures. Our work different from improving performance single network with convnets-based models recurrent neur...