Skeleton-Based Transformer for Classification of Errors and Better Feedback in Low Back Pain Physical Rehabilitation Exercises

• URL: http://arxiv.org/abs/2504.13866v1
• Date: Fri, 28 Mar 2025 10:30:39 GMT
• Title: Skeleton-Based Transformer for Classification of Errors and Better Feedback in Low Back Pain Physical Rehabilitation Exercises
• Authors: Aleksa Marusic, Sao Mai Nguyen, Adriana Tapus,
• Abstract summary: In recent years, there has been great progress in quality assessment of physical rehabilitation exercises.<n>Most of them only provide a binary classification if the performance is correct or incorrect, and a few provide a continuous score.<n>In this work, we propose an algorithm for error classification of rehabilitation exercises, thus making the first step toward more detailed feedback to patients.
• Score: 0.9094127664014627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physical rehabilitation exercises suggested by healthcare professionals can help recovery from various musculoskeletal disorders and prevent re-injury. However, patients' engagement tends to decrease over time without direct supervision, which is why there is a need for an automated monitoring system. In recent years, there has been great progress in quality assessment of physical rehabilitation exercises. Most of them only provide a binary classification if the performance is correct or incorrect, and a few provide a continuous score. This information is not sufficient for patients to improve their performance. In this work, we propose an algorithm for error classification of rehabilitation exercises, thus making the first step toward more detailed feedback to patients. We focus on skeleton-based exercise assessment, which utilizes human pose estimation to evaluate motion. Inspired by recent algorithms for quality assessment during rehabilitation exercises, we propose a Transformer-based model for the described classification. Our model is inspired by the HyperFormer method for human action recognition, and adapted to our problem and dataset. The evaluation is done on the KERAAL dataset, as it is the only medical dataset with clear error labels for the exercises, and our model significantly surpasses state-of-the-art methods. Furthermore, we bridge the gap towards better feedback to the patients by presenting a way to calculate the importance of joints for each exercise.

Related papers

• Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment [66.6041949490137]
  We propose a method that integrates information from transcribed verbal feedback and corresponding surgical video to predict feedback effectiveness. Our findings show that both transcribed feedback and surgical video are individually predictive of trainee behavior changes. Our results demonstrate the potential of multi-modal learning to advance the automated assessment of surgical feedback.
  arXiv  Detail & Related papers  (2024-11-17T00:13:00Z)
• A Medical Low-Back Pain Physical Rehabilitation Dataset for Human Body Movement Analysis [0.6990493129893111]
  This article addresses four challenges to address and propose a medical dataset of clinical patients carrying out low back-pain rehabilitation exercises.<n>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.
  arXiv  Detail & Related papers  (2024-06-29T19:50:06Z)
• Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives [2.166000001057538]
  Exercise-based rehabilitation programs have proven to be effective in enhancing the quality of life and reducing mortality and rehospitalization rates. These programs commonly prescribe a variety of exercise types, leading to a distinct challenge in rehabilitation exercise assessment datasets. This paper introduces a novel supervised contrastive learning framework with hard and soft negative samples to train a single model applicable to all exercise types.
  arXiv  Detail & Related papers  (2024-03-05T08:38:25Z)
• Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment [3.544570529705401]
  Exercise-based rehabilitation programs have been shown to enhance quality of life and reduce mortality and rehospitalizations. AI-driven virtual rehabilitation programs enable patients to complete exercises independently at home while AI algorithms can analyze exercise data to provide feedback to patients and report their progress to clinicians. This paper introduces a novel approach to assessing the quality of rehabilitation exercises using RGB video. Sequences of skeletal body joints are extracted from consecutive RGB video frames and analyzed by many-to-one sequential neural networks to evaluate exercise quality.
  arXiv  Detail & Related papers  (2023-06-15T23:23:35Z)
• Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises [68.37238218842089]
  We develop an interactive social robot exercise coaching system for personalized rehabilitation. This system integrates a neural network model with a rule-based model to automatically monitor and assess patients' rehabilitation exercises. Our system can adapt to new participants and achieved 0.81 average performance to assess their exercises, which is comparable to the experts' agreement level.
  arXiv  Detail & Related papers  (2023-05-12T17:37:04Z)
• Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints [6.918076156491651]
  This paper presents a novel approach for segmenting and counting the repetitions of rehabilitation exercises performed by patients. Skeletal body joints can be acquired through depth cameras or computer vision techniques applied to RGB videos of patients. Various sequential neural networks are designed to analyze the sequences of skeletal body joints and perform repetition segmentation and counting.
  arXiv  Detail & Related papers  (2023-04-19T15:22:15Z)
• Vogtareuth Rehab Depth Datasets: Benchmark for Marker-less Posture Estimation in Rehabilitation [55.41644538483948]
  We propose two rehabilitation-specific pose datasets containing depth images and 2D pose information of patients performing rehab exercises. We use a state-of-the-art marker-less posture estimation model which is trained on a non-rehab benchmark dataset. We show that our dataset can be used to train pose models to detect rehab-specific complex postures.
  arXiv  Detail & Related papers  (2021-08-23T16:18:26Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)
• Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation [51.72616167073565]
  We propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance.
  arXiv  Detail & Related papers  (2020-06-28T21:03:19Z)
• A Review of Computational Approaches for Evaluation of Rehabilitation Exercises [58.720142291102135]
  This paper reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches.
  arXiv  Detail & Related papers  (2020-02-29T22:18:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.