# Human Gait Recognition Using Bag of Words Feature Representation Method

arXiv: https://arxiv.org/abs/2203.13317 · HTML: https://mlsanity.com/paper/2203.13317/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [Human Gait Database for Normal Walk Collected by Smartphone Accelerometer](https://mlsanity.com/paper/1905.03109/) (2019)
- [Feature Learning for Accelerometer based Gait Recognition](https://mlsanity.com/paper/2007.15958/) (2020)

## Built on by (most cited first, 3 of 3)

- [Particle Swarm Optimization Based Demand Response Using Artificial Neural Network Based Load Prediction](https://mlsanity.com/paper/2204.13990/) (2022)
- [Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans](https://mlsanity.com/paper/2301.09667/) (2023)
- [Human Gait Recognition using Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2309.10144/) (2023)

## Abstract (arXiv)

In this paper, we propose a novel gait recognition method based on a bag-of-words feature representation method. The algorithm is trained, tested and evaluated on a unique human gait data consisting of 93 individuals who walked with comfortable pace between two end points during two different sessions. To evaluate the effectiveness of the proposed model, the results are compared with the outputs of the classification using extracted features. As it is presented, the proposed method results in significant improvement accuracy compared to using common statistical features, in all the used classifiers.