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

arXiv: https://arxiv.org/abs/1703.02931 · HTML: https://mlsanity.com/paper/1703.02931/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2017.

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

- [How do Foundation Models Compare to Skeleton-Based Approaches for Gesture Recognition in Human-Robot Interaction?](https://mlsanity.com/paper/2506.20795/) (2025)
- [Real-Time Online Skeleton Extraction and Gesture Recognition on Pepper](https://mlsanity.com/paper/2206.11376/) (2022)

## Abstract (arXiv)

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.