# Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises

arXiv: https://arxiv.org/abs/2408.02855 · HTML: https://mlsanity.com/paper/2408.02855/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 63 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 22 of 22)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition](https://mlsanity.com/paper/1607.07043/) (2016)
- [A New Representation of Skeleton Sequences for 3D Action Recognition](https://mlsanity.com/paper/1703.03492/) (2017)
- [Skeleton-Aware Networks for Deep Motion Retargeting](https://mlsanity.com/paper/2005.05732/) (2020)
- [BlazePose: On-device Real-time Body Pose tracking](https://mlsanity.com/paper/2006.10204/) (2020)
- [Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks](https://mlsanity.com/paper/2008.07404/) (2020)
- [Interpretable 3D Human Action Analysis with Temporal Convolutional Networks](https://mlsanity.com/paper/1704.04516/) (2017)
- [Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks](https://mlsanity.com/paper/1611.02447/) (2016)
- [A Deep Learning Framework for Assessing Physical Rehabilitation Exercises](https://mlsanity.com/paper/1901.10435/) (2019)
- [Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints](https://mlsanity.com/paper/1909.05704/) (2019)
- [Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition](https://mlsanity.com/paper/2107.00606/) (2021)
- [A Review of Computational Approaches for Evaluation of Rehabilitation Exercises](https://mlsanity.com/paper/2003.08767/) (2020)
- [A co-design approach for a rehabilitation robot coach for physical rehabilitation based on the error classification of motion errors](https://mlsanity.com/paper/2111.09729/) (2021)
- [A Medical Low-Back Pain Physical Rehabilitation Dataset for Human Body Movement Analysis](https://mlsanity.com/paper/2407.00521/) (2024)
- [Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand their Physical Limitations](https://mlsanity.com/paper/1810.04879/) (2018)

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

- [Skeleton-Based Transformer for Classification of Errors and Better Feedback in Low Back Pain Physical Rehabilitation Exercises](https://mlsanity.com/paper/2504.13866/) (2025)

## Abstract (arXiv)

Analyzing human motion is an active research area, with various applications. In this work, we focus on human motion analysis in the context of physical rehabilitation using a robot coach system. Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system, such as RGB and RGB-D cameras. As 2D and 3D human pose estimation from RGB images had made impressive improvements, we aim to compare the assessment of physical rehabilitation exercises using movement data obtained from both RGB-D camera (Microsoft Kinect) and estimation from RGB videos (OpenPose and BlazePose algorithms). A Gaussian Mixture Model (GMM) is employed from position (and orientation) features, with performance metrics defined based on the log-likelihood values from GMM. The evaluation is performed on a medical database of clinical patients carrying out low back-pain rehabilitation exercises, previously coached by robot Poppy.