# Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movements

arXiv: https://arxiv.org/abs/2503.03717 · HTML: https://mlsanity.com/paper/2503.03717/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 264 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 27 of 27)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model](https://mlsanity.com/paper/1605.03170/) (2016)
- [Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods](https://mlsanity.com/paper/2006.01423/) (2020)
- [MoVi: A Large Multipurpose Motion and Video Dataset](https://mlsanity.com/paper/2003.01888/) (2020)
- [Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG](https://mlsanity.com/paper/2207.01435/) (2022)
- [Explaining the Unique Nature of Individual Gait Patterns with Deep Learning](https://mlsanity.com/paper/1808.04308/) (2018)
- [HuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor Networks](https://mlsanity.com/paper/1705.08506/) (2017)
- [On the Explanation of Machine Learning Predictions in Clinical Gait Analysis](https://mlsanity.com/paper/1912.07737/) (2019)
- [Measuring and modeling the motor system with machine learning](https://mlsanity.com/paper/2103.11775/) (2021)
- [Automatic Classification of Functional Gait Disorders](https://mlsanity.com/paper/1712.06405/) (2017)
- [KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis](https://mlsanity.com/paper/1707.06105/) (2017)
- [Bounded logit attention: Learning to explain image classifiers](https://mlsanity.com/paper/2105.14824/) (2021)
- [Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models through a Single Waist-worn Wearable Sensor](https://mlsanity.com/paper/2203.00503/) (2022)
- [Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy](https://mlsanity.com/paper/2208.05232/) (2022)
- [Explaining machine learning models for age classification in human gait analysis](https://mlsanity.com/paper/2211.17016/) (2022)
- [Self-Expressive Subspace Clustering to Recognize Motion Dynamics of a Multi-Joint Coordination for Chronic Ankle Instability](https://mlsanity.com/paper/1901.01558/) (2019)
- [Explaining automated gender classification of human gait](https://mlsanity.com/paper/2211.17015/) (2022)

## Built on by (most cited first, 1 of 1)

- [Outlier Detection in Plantar Pressure: Human-Centered Comparison of Statistical Parametric Mapping and Explainable Machine Learning](https://mlsanity.com/paper/2509.21943/) (2025)

## Abstract (arXiv)

This chapter provides an overview of recent and promising Machine Learning applications, i.e. pose estimation, feature estimation, event detection, data exploration & clustering, and automated classification, in gait (walking and running) and sports biomechanics. It explores the potential of Machine Learning methods to address challenges in biomechanical workflows, highlights central limitations, i.e. data and annotation availability and explainability, that need to be addressed, and emphasises the importance of interdisciplinary approaches for fully harnessing the potential of Machine Learning in gait and sports biomechanics.