# Transforming Gait: Video-Based Spatiotemporal Gait Analysis

arXiv: https://arxiv.org/abs/2203.09371 · HTML: https://mlsanity.com/paper/2203.09371/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Going deeper with Image Transformers](https://mlsanity.com/paper/2103.17239/) (2021)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [HuMoR: 3D Human Motion Model for Robust Pose Estimation](https://mlsanity.com/paper/2105.04668/) (2021)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [Distribution-Aware Coordinate Representation for Human Pose Estimation](https://mlsanity.com/paper/1910.06278/) (2019)
- [GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras](https://mlsanity.com/paper/2112.01524/) (2021)
- [SimPoE: Simulated Character Control for 3D Human Pose Estimation](https://mlsanity.com/paper/2104.00683/) (2021)
- [PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation](https://mlsanity.com/paper/2105.02465/) (2021)
- [Deep Cosine Metric Learning for Person Re-Identification](https://mlsanity.com/paper/1812.00442/) (2018)
- [Neural Monocular 3D Human Motion Capture with Physical Awareness](https://mlsanity.com/paper/2105.01057/) (2021)
- [Movement science needs different pose tracking algorithms](https://mlsanity.com/paper/1907.10226/) (2019)
- [PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research](https://mlsanity.com/paper/2203.08792/) (2022)
- [LiftFormer: 3D Human Pose Estimation using attention models](https://mlsanity.com/paper/2009.00348/) (2020)
- [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, 8 of 8)

- [Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone](https://mlsanity.com/paper/2507.08268/) (2025)
- [Self-Supervised Learning of Gait-Based Biomarkers](https://mlsanity.com/paper/2307.16321/) (2023)
- [Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia](https://mlsanity.com/paper/2308.11484/) (2023)
- [Learning to Estimate Critical Gait Parameters from Single-View RGB Videos with Transformer-Based Attention Network](https://mlsanity.com/paper/2312.00398/) (2023)
- [Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulation](https://mlsanity.com/paper/2402.12676/) (2024)
- [Monocular Markerless Motion Capture Enables Quantitative Assessment of Upper Extremity Reachable Workspace](https://mlsanity.com/paper/2602.13176/) (2026)
- [BiomechAgent: AI-Assisted Biomechanical Analysis Through Code-Generating Agents](https://mlsanity.com/paper/2602.06975/) (2026)
- [SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation](https://mlsanity.com/paper/2609.08108/) (2026)

## Abstract (arXiv)

Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation. This potential is tempered by the smaller body of work ensuring the outputs are clinically meaningful and properly calibrated. Gait analysis, typically performed in a dedicated lab, produces precise measurements including kinematics and step timing. Using over 7000 monocular video from an instrumented gait analysis lab, we trained a neural network to map 3D joint trajectories and the height of individuals onto interpretable biomechanical outputs including gait cycle timing and sagittal plane joint kinematics and spatiotemporal trajectories. This task specific layer produces accurate estimates of the timing of foot contact and foot off events. After parsing the kinematic outputs into individual gait cycles, it also enables accurate cycle-by-cycle estimates of cadence, step time, double and single support time, walking speed and step length.