Low Power Gesture Recognition

Contactless Gesture Recognition for Mobile Devices

While gesture interfaces become pervasive, most existing approaches are undesirable for mobile devices because of the high power consumption, or the inconvenience that users need to wear/hold specific sensors. In this paper, we present a contactless gesture recognition system for mobile devices using proximity sensors. A set of infrared signal feature extraction methods and a decision-tree-base...

Bringing Gesture Recognition to All Devices

Existing gesture-recognition systems consume significant power and computational resources that limit how they may be used in low-end devices. We introduce AllSee, the first gesture-recognition system that can operate on a range of computing devices including those with no batteries. AllSee consumes three to four orders of magnitude lower power than state-of-the-art systems and can enable alway...

Gesture Recognition Using the Linear Combination of Membership Degrees of Observations

A Two-Level Approach for Modeling and Recognition of Hand Gesture

The main characteristics of human hand gesture can be summarized by its dynamic, multi-attribute property. Previous research for gesture pattern interpretation have shown the possibility for the recognition of local aspects of hand gesture. But global framework for finding the whole interpretation from the local aspects has yet to be provided. In this article, we proposed a two-level approach u...

Applying mean shift and motion detection approaches to hand tracking in sign language

Hand gesture recognition is very important to communicate in sign language. In this paper, an effective object tracking and hand gesture recognition method is proposed. This method is combination of two well-known approaches, the mean shift and the motion detection algorithm. The mean shift algorithm can track objects based on the color, then when hand passes the face occlusion happens. Several...