# Learning to Estimate Critical Gait Parameters from Single-View RGB Videos with Transformer-Based Attention Network

arXiv: https://arxiv.org/abs/2312.00398 · HTML: https://mlsanity.com/paper/2312.00398/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Movement science needs different pose tracking algorithms](https://mlsanity.com/paper/1907.10226/) (2019)
- [Transforming Gait: Video-Based Spatiotemporal Gait Analysis](https://mlsanity.com/paper/2203.09371/) (2022)
- [Algorithm Based on One Monocular Video Delivers Highly Valid and Reliable Gait Parameters](https://mlsanity.com/paper/2008.08045/) (2020)

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

- [Quantitative Gait Analysis from Single RGB Videos Using a Dual-Input Transformer-Based Network](https://mlsanity.com/paper/2501.01689/) (2025)
- [SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation](https://mlsanity.com/paper/2609.08108/) (2026)

## Abstract (arXiv)

Musculoskeletal diseases and cognitive impairments in patients lead to difficulties in movement as well as negative effects on their psychological health. Clinical gait analysis, a vital tool for early diagnosis and treatment, traditionally relies on expensive optical motion capture systems. Recent advances in computer vision and deep learning have opened the door to more accessible and cost-effective alternatives. This paper introduces a novel spatio-temporal Transformer network to estimate critical gait parameters from RGB videos captured by a single-view camera. Empirical evaluations on a public dataset of cerebral palsy patients indicate that the proposed framework surpasses current state-of-the-art approaches and show significant improvements in predicting general gait parameters (including Walking Speed, Gait Deviation Index - GDI, and Knee Flexion Angle at Maximum Extension), while utilizing fewer parameters and alleviating the need for manual feature extraction.