Publications by authors named "Shinji Eto"
Low levels of physical activity in individuals with chronic pain can lead to additional functional impairment and disability. This study aims to investigate the predictors of low physical activity levels in individuals with chronic pain, and to determine the accuracy of the artificial neural network used to analyze these predictors. Community-dwelling older adults with chronic pain (n = 103) were surveyed for their physical activity levels and classified into low, moderate, or high physical activity level groups.
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- * A machine learning tool was developed to analyze images of feet and detect hallux valgus, with tests utilizing 507 images and two different preprocessing patterns (A and B).
- * The study found that Pattern B preprocessing yielded better accuracy and performance metrics than Pattern A, indicating the potential for this tool to effectively screen for hallux valgus with further improvements.