# Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D Skeletons

## TL;DR

This paper introduces a novel double-stage classification method using Multiple Stream Discrete Hidden Markov Models on 3D skeleton data, achieving high accuracy and real-time performance in gesture recognition.

## Contribution

The paper presents a new MSD-HMM based approach for gesture recognition that combines offline and online classification with high efficiency and state-of-the-art accuracy.

## Key findings

- Achieved state-of-the-art results on multiple public datasets.
- Performed real-time online gesture segmentation and classification.
- Demonstrated robustness across various datasets including a new HCI-specific dataset.

## Abstract

HMMs are widely used in action and gesture recognition due to their implementation simplicity, low computational requirement, scalability and high parallelism. They have worth performance even with a limited training set. All these characteristics are hard to find together in other even more accurate methods. In this paper, we propose a novel double-stage classification approach, based on Multiple Stream Discrete Hidden Markov Models (MSD-HMM) and 3D skeleton joint data, able to reach high performances maintaining all advantages listed above. The approach allows both to quickly classify pre-segmented gestures (offline classification), and to perform temporal segmentation on streams of gestures (online classification) faster than real time. We test our system on three public datasets, MSRAction3D, UTKinect-Action and MSRDailyAction, and on a new dataset, Kinteract Dataset, explicitly created for Human Computer Interaction (HCI). We obtain state of the art performances on all of them.