# Locomotion and gesture tracking in mice and small animals for neurosceince applications: A survey

## TL;DR

This survey reviews automated computer vision and machine learning methods for tracking locomotion and gestures in mice and small animals, highlighting their hardware/software approaches, strengths, and weaknesses to improve neuroscience research.

## Contribution

It provides a comprehensive categorization and analysis of existing automated tracking methods for small animals in neuroscience, emphasizing their advantages and limitations.

## Key findings

- Most approaches use computer vision and machine learning techniques.
- Hardware choices vary, affecting accuracy and usability.
- Strengths and weaknesses of each method are summarized.

## Abstract

Neuroscience has traditionally relied on manually observing lab animals in controlled environments. Researchers usually record animals behaving in free or restrained manner and then annotate the data manually. The manual annotation is not desirable for three reasons; one, it is time consuming, two, it is prone to human errors and three, no two human annotators will 100\% agree on annotation, so it is not reproducible. Consequently, automated annotation of such data has gained traction because it is efficient and replicable. Usually, the automatic annotation of neuroscience data relies on computer vision and machine leaning techniques. In this article, we have covered most of the approaches taken by researchers for locomotion and gesture tracking of lab animals. We have divided these papers in categories based upon the hardware they use and the software approach they take. We also have summarized their strengths and weaknesses.