Sleep Monitoring Wear Compliance Classification Image Dataset
#image classification #deep learning training #medical imaging analysis #sleep research #wear compliance monitoring
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-18
AI Analysis & Value Prop
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.
Dataset Insights
Sample Examples
d97e96ed**.jpg|1080*1440|268.89 KB
17b3c0a0**.jpg|1080*1440|181.25 KB
aec119dd**.jpg|1080*1384|191.28 KB
dcf2c1ca**.jpg|1280*1706|454.73 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| wearing_status | string | The status of device wearing shown in the image, such as correctly worn, incorrectly worn, not worn, etc. |
| equipment_visibility | string | The visibility level of the equipment in the image, such as fully visible, partially visible, not visible. |
| user_position | string | The position of the user in the image, for example, lying down, side sleeping, sitting, etc. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What is the sleep monitoring compliance classification image dataset?
- The sleep monitoring compliance classification image dataset is a collection of images designed to analyze and classify the compliance of wearing sleep monitoring devices, primarily used for image classification tasks in the healthcare sector.
- How does this dataset assist in training and developing deep learning models?
- This dataset provides a rich set of image samples to help machine learning and deep learning models accurately recognize and determine the compliance of wearing sleep monitoring devices, enhancing model accuracy and practicality.
- In which fields is the sleep monitoring compliance dataset mainly applied?
- This dataset is mainly applied in the healthcare sector, particularly in research and applications related to sleep studies, and the development and use of monitoring devices.
- Why choose image data to study sleep monitoring compliance?
- Image data can intuitively reflect information such as how and where the devices are worn, enabling machine learning models to better judge and analyze compliance.
- What role does the dataset play in improving healthcare technology?
- By analyzing compliance data, the dataset can enhance the accuracy of sleep monitoring and the adherence to device usage, thus driving the development of more advanced healthcare monitoring technologies.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Sleep Monitoring Wear Compliance Classification Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/f65876ce46fc06e73af2d74a7cc9121d},
urldate={2025-10-22},
keywords={sleep monitoring, wear compliance, image classification dataset, medical imaging data},
version={1.0}
}Using this in research? Please cite us.
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