Generating a novel synthetic dataset for rehabilitation exercises using pose-guided conditioned diffusion models: A quantitative and qualitative evaluation

• Published in: Computers in biology and medicine Vol. 167; p. 107665
• Main Authors: Mennella, Ciro, Maniscalco, Umberto, De Pietro, Giuseppe, Esposito, Massimo
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2023; Elsevier Limited
• Subjects: Aged; Artificial Intelligence; Biomechanics; Computer vision; Consistency; Datasets; Deep learning; Diffusion models; Exercise; Exercise Therapy - methods; Generative models; Human motion; Humans; Image enhancement; Image processing; Internal Medicine; Learning algorithms; Machine Learning; Movement; Other; Pose estimation; Rehabilitation; Synthetic data; Deep learning; Computer vision; Rehabilitation; Generative models; Pose estimation; Artificial Intelligence
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107665

Machine learning has emerged as a promising approach to enhance rehabilitation therapy monitoring and evaluation, providing personalized insights. However, the scarcity of data remains a significant challenge in developing robust machine learning models for rehabilitation. This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises. A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90. This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises. •Enriching real-world rehabilitation datasets through synthetic data augmentation.•Advanced generative algorithms for creating novel, high-fidelity images that capture essential biomechanical attributes.•Introducing comprehensive evaluation methods for validating the consistency of generated synthetic data in the rehabilitation field.