首个面向家庭场景的多角度视频与生理信号同步数据集。
MHAD: Multimodal Home Activity Dataset with Multi-Angle Videos and Synchronized Physiological Signals
- 采集40人6类日常活动，3个视角的1440段真实家庭环境视频。
- 同步记录5种生理信号，支持脉搏、呼吸等非接触式监测研究。
- 兼容rPPG工具箱，适合居家健康监测算法验证与开发。
基于视频的生理监测技术，如远程光电容积脉搏波描记法（rPPG），通过分析视频中细微变化来提取心率、呼吸等生理信号，具有非接触、实时监测的优势，适用于家庭环境。尽管已有公开基准数据集推动该技术发展，但目前尚无专为被动家庭监测设计的数据集。现有数据集通常仅包含近距离、静态、正面拍摄的视频，且仅涵盖1-2种生理信号。为推进真实家庭场景下的视频生理监测技术，我们提出MHAD数据集，包含40名受试者在真实家庭环境中完成6类典型活动的1,440段视频，从3个角度录制，并同步采集5种生理信号，构成全面的视频生理数据集。该数据集与rPPG-toolbox兼容，已通过多种无监督和有监督方法验证。数据集已公开于https://github.com/jdh-algo/MHAD-Dataset。
原文摘要 · Abstract (English)
Video-based physiology, exemplified by remote photoplethysmography (rPPG), extracts physiological signals such as pulse and respiration by analyzing subtle changes in video recordings. This non-contact, real-time monitoring method holds great potential for home settings. Despite the valuable contributions of public benchmark datasets to this technology, there is currently no dataset specifically designed for passive home monitoring. Existing datasets are often limited to close-up, static, frontal recordings and typically include only 1-2 physiological signals. To advance video-based physiology in real home settings, we introduce the MHAD dataset. It comprises 1,440 videos from 40 subjects, capturing 6 typical activities from 3 angles in a real home environment. Additionally, 5 physiological signals were recorded, making it a comprehensive video-based physiology dataset. MHAD is compatible with the rPPG-toolbox and has been validated using several unsupervised and supervised methods. Our dataset is publicly available at https://github.com/jdh-algo/MHAD-Dataset.
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