Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives

• URL: http://arxiv.org/abs/2403.02772v2
• Date: Fri, 9 Aug 2024 15:54:49 GMT
• Title: Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives
• Authors: Mark Karlov, Ali Abedi, Shehroz S. Khan,
• Abstract summary: 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.
• Score: 2.166000001057538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Exercise-based rehabilitation programs have proven to be effective in enhancing the quality of life and reducing mortality and rehospitalization rates. AI-driven virtual rehabilitation, which allows patients to independently complete exercises at home, utilizes AI algorithms to analyze exercise data, providing feedback to patients and updating clinicians on their progress. These programs commonly prescribe a variety of exercise types, leading to a distinct challenge in rehabilitation exercise assessment datasets: while abundant in overall training samples, these datasets often have a limited number of samples for each individual exercise type. This disparity hampers the ability of existing approaches to train generalizable models with such a small sample size per exercise type. Addressing this issue, 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. This model, with a Spatial-Temporal Graph Convolutional Network (ST-GCN) architecture, demonstrated enhanced generalizability across exercises and a decrease in overall complexity. Through extensive experiments on three publicly available rehabilitation exercise assessment datasets, UI-PRMD, IRDS, and KIMORE, our method has proven to surpass existing methods, setting a new benchmark in rehabilitation exercise quality assessment.

Related papers

• Intelligent Repetition Counting for Unseen Exercises: A Few-Shot Learning Approach with Sensor Signals [0.4998632546280975]
  This study develops a method to automatically count exercise repetitions by analyzing IMU signals. We propose a repetition counting technique utilizing a deep metric-based few-shot learning approach. We show an 86.8% probability of accurately counting ten or more repetitions within a single set across 28 different exercises.
  arXiv  Detail & Related papers  (2024-10-01T05:04:40Z)
• 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. 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)
• Distilled Datamodel with Reverse Gradient Matching [74.75248610868685]
  We introduce an efficient framework for assessing data impact, comprising offline training and online evaluation stages. Our proposed method achieves comparable model behavior evaluation while significantly speeding up the process compared to the direct retraining method.
  arXiv  Detail & Related papers  (2024-04-22T09:16:14Z)
• 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)
• 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)
• Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing [53.096237570992294]
  Strategy training is a rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Standardized fidelity assessment is used to measure adherence to treatment principles. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task.
  arXiv  Detail & Related papers  (2022-09-14T15:33:30Z)
• 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)
• Robustness Gym: Unifying the NLP Evaluation Landscape [91.80175115162218]
  Deep neural networks are often brittle when deployed in real-world systems. Recent research has focused on testing the robustness of such models. We propose a solution in the form of Robustness Gym, a simple and evaluation toolkit.
  arXiv  Detail & Related papers  (2021-01-13T02:37:54Z)
• 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.