Computer Vision-Based Approach for Breast Cancer Rehabilitation Evaluation: A Survey

Abstract

The number of breast cancer survivors living several decades after their diagnosis is increasing, which means there is a greater need for effective rehabilitation programs. While solid evidence suggests that safe exercise can improve quality of life and reduce the side effects of cancer treatments, recent research has revealed that patients may struggle to perform suggested physical exercises, particularly those in home-based rehabilitation programs. Most patients do not meet recommended levels of activity due to a lack of confidence in safety and a lack of supervision and evaluation. As a result, computer vision-based approaches have increased use for monitoring and evaluating exercise performance. Reliable motion capture sensors with advanced machine learning capabilities provide opportunities for systematic and standardized evaluation systems. This survey explores the literature on computer vision-based approaches to rehabilitation evaluation systems, including data collection with motion capture sensors and public datasets, feature extraction and representation, and feature comparison for evaluation. The study also reviews existing rehabilitation systems by comparing their data collection methods and findings. Additionally, the paper discusses challenges and recommendations related to this topic for further research.