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A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis
Insights
This study introduces an augmented reality system for assessing juvenile dermatomyositis (JDM) muscle strength. The system combines an action quality assessment algorithm with a virtual character for more accurate and efficient human evaluation.
Area of Science:
- Biomedical Engineering
- Computer Vision
- Pediatric Rheumatology
Background:
- Juvenile dermatomyositis (JDM) is a common childhood inflammatory myopathy causing muscle weakness and skin rashes.
- Current muscle strength assessment methods like the Childhood Myositis Assessment Scale (CMAS) lack scalability and can be subjective.
- Existing automatic action quality assessment (AQA) algorithms lack the necessary accuracy for critical biomedical applications.
Purpose of the Study:
- To develop a scalable and objective system for assessing muscle strength in children with JDM.
- To integrate an advanced AQA algorithm with augmented reality (AR) for human-in-the-loop verification.
- To improve the accuracy and efficiency of JDM muscle strength assessment.
Main Methods:
- Developed a contrastive regression AQA algorithm trained on a JDM dataset for muscle strength assessment.
- Created a virtual character visualization using a 3D animation dataset to represent AQA results.
- Implemented a video-based AR system that integrates computer vision for scene understanding and optimal virtual character augmentation.
- Highlighted key visual elements within the AR system to facilitate human verification of AQA outputs.
Main Results:
- The proposed AQA algorithm demonstrated effectiveness in assessing JDM muscle strength.
- User studies confirmed that the AR system significantly improved the accuracy and speed of muscle strength assessment.
- The system enables users to effectively compare real-world patient movements with virtual character representations.
Conclusions:
- The developed video-based AR system offers a promising solution for objective and efficient muscle strength assessment in JDM.
- Human-in-the-loop verification, facilitated by AR visualization, enhances the reliability of automated muscle strength evaluations.
- This approach addresses the limitations of traditional assessment methods and current AQA technologies in pediatric rheumatology.
Abstract:
As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle involvement for diagnosis or rehabilitation monitoring. On the one hand, human diagnosis is not scalable and may be subject to personal bias. On the other hand, automatic action quality assessment (AQA) algorithms cannot guarantee 100% accuracy, making them not suitable for biomedical applications. As a solution, we propose a video-based augmented reality system for human-in-the-loop muscle strength assessment of children with JDM. We first propose an AQA algorithm for muscle strength assessment of JDM using contrastive regression trained by a JDM dataset. Our core insight is to visualize the AQA results as a virtual character facilitated by a 3D animation dataset, so that users can compare the real-world patient and the virtual character to understand and verify the AQA results. To allow effective comparisons, we propose a video-based augmented reality system. Given a feed, we adapt computer vision algorithms for scene understanding, evaluate the optimal way of augmenting the virtual character into the scene, and highlight important parts for effective human verification. The experimental results confirm the effectiveness of our AQA algorithm, and the results of the user study demonstrate that humans can more accurately and quickly assess the muscle strength of children using our system.
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