Sleep Monitoring Wear Correctness Classification Image Dataset

The current medical industry faces issues with insufficient accuracy of wearable devices in the field of sleep monitoring, leading to unreliable monitoring results and affecting patient treatment efficacy. Existing solutions mainly rely on simple assessments of the wearing state, lacking in-depth analysis and classification of image data. This dataset aims to solve the accuracy problem of wearable devices in sleep monitoring through image classification technology, providing more precise recognition of wearing status. Data is collected using high-resolution camera equipment in real-world usage environments to ensure image quality. To ensure high-quality data, we implemented multiple rounds of annotation and consistency checks, and the accuracy of data annotations was finally confirmed through expert review. Data is stored in JPG format and organized by category for ease of subsequent use and analysis.