Real-Time and Continuous Hand Gesture Spotting: an Approach Based on Artificial Neural Networks
This work addresses gesture recognition for industrial robot control, presenting an incremental improvement with specific performance gains.
The paper tackled continuous real-time hand gesture spotting for human-robot interfaces by proposing a new architecture with two artificial neural networks in series to recognize communicative and noncommunicative gestures, achieving over 99% recognition for ten gestures and over 96% for thirty gestures.
New and more natural human-robot interfaces are of crucial interest to the evolution of robotics. This paper addresses continuous and real-time hand gesture spotting, i.e., gesture segmentation plus gesture recognition. Gesture patterns are recognized by using artificial neural networks (ANNs) specifically adapted to the process of controlling an industrial robot. Since in continuous gesture recognition the communicative gestures appear intermittently with the noncommunicative, we are proposing a new architecture with two ANNs in series to recognize both kinds of gesture. A data glove is used as interface technology. Experimental results demonstrated that the proposed solution presents high recognition rates (over 99% for a library of ten gestures and over 96% for a library of thirty gestures), low training and learning time and a good capacity to generalize from particular situations.