Sleep Monitoring Wear Compliance Classification Image Dataset

With increasing attention to sleep health, the popularity of sleep monitoring devices has made wear compliance an important issue. However, most devices currently rely on manual analysis for monitoring wear compliance, which is inefficient and prone to errors. Therefore, we have established this sleep monitoring wear compliance classification image dataset, aiming to enhance the automatic recognition capability of wear compliance through machine learning technology. The dataset includes 5000 labeled images, covering samples of different wear states. Data collection used standardized sleep monitoring devices to ensure a uniform collection environment and reduce external interference. Each image underwent multiple rounds of annotation and consistency checks to ensure annotation accuracy. The data is stored in JPG format, organized by category, facilitating subsequent model training and evaluation.