# Human Gesture and Gait Analysis for Autism Detection

arXiv: https://arxiv.org/abs/2304.08368 · HTML: https://mlsanity.com/paper/2304.08368/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [One-shot action recognition in challenging therapy scenarios](https://mlsanity.com/paper/2102.08997/) (2021)
- [Skepxels: Spatio-temporal Image Representation of Human Skeleton Joints for Action Recognition](https://mlsanity.com/paper/1711.05941/) (2017)

## Built on by (most cited first, 4 of 4)

- [UAL-Bench: The First Comprehensive Unusual Activity Localization Benchmark](https://mlsanity.com/paper/2410.01180/) (2024)
- [Emotion Recognition from Skeleton Data: A Comprehensive Survey](https://mlsanity.com/paper/2507.18026/) (2025)
- [Gait Data Augmentation using Physics-Based Biomechanical Simulation](https://mlsanity.com/paper/2307.08092/) (2023)
- [Physics Augmented Tuple Transformer for Autism Severity Level Detection](https://mlsanity.com/paper/2409.18438/) (2024)

## Abstract (arXiv)

Autism diagnosis presents a major challenge due to the vast heterogeneity of the condition and the elusive nature of early detection. Atypical gait and gesture patterns are dominant behavioral characteristics of autism and can provide crucial insights for diagnosis. Furthermore, these data can be collected efficiently in a non-intrusive way, facilitating early intervention to optimize positive outcomes. Existing research mainly focuses on associating facial and eye-gaze features with autism. However, very few studies have investigated movement and gesture patterns which can reveal subtle variations and characteristics that are specific to autism. To address this gap, we present an analysis of gesture and gait activity in videos to identify children with autism and quantify the severity of their condition by regressing autism diagnostic observation schedule scores. Our proposed architecture addresses two key factors: (1) an effective feature representation to manifest irregular gesture patterns and (2) a two-stream co-learning framework to enable a comprehensive understanding of its relation to autism from diverse perspectives without explicitly using additional data modality. Experimental results demonstrate the efficacy of utilizing gesture and gait-activity videos for autism analysis.