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

TL;DR

This paper introduces a comprehensive dataset for low-back pain rehabilitation exercises, including multimodal data and annotations, to facilitate development of intelligent systems for clinical movement assessment and error detection.

Contribution

It provides a novel, clinically relevant dataset with multimodal data and annotations, along with baseline evaluations of movement recognition algorithms for rehabilitation.

Findings

The dataset includes 3D skeleton, RGB videos, and medical annotations.

Baseline algorithms (GMM and LSTM) show potential for movement recognition.

The dataset demonstrates the feasibility of using low-cost sensors in clinical rehab settings.

Abstract

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.