Finger Angle-Based Hand Gesture Recognition for Smart Infrastructure Using Wearable Wrist-Worn Camera

The arising of domestic robots in smart infrastructure has raised demands for intuitive and natural interaction between humans and robots. To address this problem, a wearable wrist-worn camera (WwwCam) is proposed in this paper. With the capability of recognizing human hand gestures in real-time, it enables services such as controlling mopping robots, mobile manipulators, or appliances in smart-home scenarios. The recognition is based on finger segmentation and template matching. Distance transformation algorithm is adopted and adapted to robustly segment fingers from the hand. Based on fingers' angles relative to the wrist, a finger angle prediction algorithm and a template matching metric are proposed. All possible gesture types of the captured image are first predicted, and then evaluated and compared to the template image to achieve the classification. Unlike other template matching methods relying highly on large training set, this scheme possesses high flexibility since it requires only one image as the template, and can classify gestures formed by different combinations of fingers. In the experiment, it successfully recognized ten finger gestures from number zero to nine defined by American Sign Language with an accuracy up to 99.38%. Its performance was further demonstrated by manipulating a robot arm using the implemented algorithms and WwwCam to transport and pile up wooden building blocks.