# Real-Time and Continuous Hand Gesture Spotting: an Approach Based on Artificial Neural Networks

arXiv: https://arxiv.org/abs/1309.2084 · HTML: https://mlsanity.com/paper/1309.2084/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2013.

## Built on by (most cited first, 2 of 2)

- [Improving novelty detection with generative adversarial networks on hand gesture data](https://mlsanity.com/paper/2304.06696/) (2023)
- [Online Recognition of Incomplete Gesture Data to Interface Collaborative Robots](https://mlsanity.com/paper/2304.06777/) (2023)

## Abstract (arXiv)

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.