# A Medical Low-Back Pain Physical Rehabilitation Dataset for Human Body Movement Analysis

arXiv: https://arxiv.org/abs/2407.00521 · HTML: https://mlsanity.com/paper/2407.00521/

## Key facts

- Cited by 5 papers in the map (data through 7 Oct 2026).
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 29 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [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)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN](https://mlsanity.com/paper/1803.04831/) (2018)
- [BlazePose: On-device Real-time Body Pose tracking](https://mlsanity.com/paper/2006.10204/) (2020)
- [PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding](https://mlsanity.com/paper/1703.07475/) (2017)
- [Pose Flow: Efficient Online Pose Tracking](https://mlsanity.com/paper/1802.00977/) (2018)
- [SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation](https://mlsanity.com/paper/2008.11469/) (2020)
- [PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimation](https://mlsanity.com/paper/2101.02471/) (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)
- [Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises](https://mlsanity.com/paper/2305.07632/) (2023)

## Built on by (most cited first, 5 of 5)

- [The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification](https://mlsanity.com/paper/2603.20352/) (2026)
- [A Standardized Benchmark for Skeleton-Based Rehabilitation Assessment Using Deep Learning](https://mlsanity.com/paper/2507.21018/) (2025)
- [Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises](https://mlsanity.com/paper/2408.02855/) (2024)
- [Skeleton-Based Transformer for Classification of Errors and Better Feedback in Low Back Pain Physical Rehabilitation Exercises](https://mlsanity.com/paper/2504.13866/) (2025)
- [Cross-view Multimodal Vision-Based Assessment Framework for Traditional Chinese Medicine Rehabilitation Training](https://mlsanity.com/paper/2606.28104/) (2026)

## Abstract (arXiv)

While automatic monitoring and coaching of exercises are showing encouraging results in non-medical applications, they still have limitations such as errors and limited use contexts. To allow the development and assessment of physical rehabilitation by an intelligent tutoring system, we identify in this article four challenges to address and propose a medical dataset of clinical patients carrying out low back-pain rehabilitation exercises. The dataset includes 3D Kinect skeleton positions and orientations, RGB videos, 2D skeleton data, and medical annotations to assess the correctness, and error classification and localisation of body part and timespan. Along this dataset, we perform a complete research path, from data collection to processing, and finally a small benchmark. We evaluated on the dataset two baseline movement recognition algorithms, pertaining to two different approaches: the probabilistic approach with a Gaussian Mixture Model (GMM), and the deep learning approach with a Long-Short Term Memory (LSTM). This dataset is valuable because it includes rehabilitation relevant motions in a clinical setting with patients in their rehabilitation program, using a cost-effective, portable, and convenient sensor, and because it shows the potential for improvement on these challenges.