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

• URL: http://arxiv.org/abs/2407.00521v1
• Date: Sat, 29 Jun 2024 19:50:06 GMT
• Title: A Medical Low-Back Pain Physical Rehabilitation Dataset for Human Body Movement Analysis
• Authors: Sao Mai Nguyen, Maxime Devanne, Olivier Remy-Neris, Mathieu Lempereur, André Thepaut,
• Abstract summary: This article addresses 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.
• Score: 0.6990493129893111
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives [2.4343669357792708]
  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 that effectively utilizes the entire dataset to train a single model applicable to all exercise types.
  arXiv  Detail & Related papers  (2024-03-05T08:38:25Z)
• D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation [0.3626013617212666]
  This paper introduces a new graph-based model for assessing rehabilitation exercises. Dense connections and GRU mechanisms are used to rapidly process large 3D skeleton inputs. The evaluation of our proposed approach on the KIMORE and UI-PRMD datasets highlighted its potential.
  arXiv  Detail & Related papers  (2023-12-21T00:38:31Z)
• 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)
• Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs [61.080598804629375]
  People with chronic pain unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities. We proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.
  arXiv  Detail & Related papers  (2022-12-20T12:56:28Z)
• Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches [5.132618393976799]
  We aim to design an upper-limb assessment pipeline for stroke patients using smartwatches. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale.
  arXiv  Detail & Related papers  (2022-12-09T14:00:49Z)
• 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)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• 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)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)

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.