可穿戴计算机
光容积图
可穿戴技术
计算机科学
时间戳
水准点(测量)
智能手表
模态(人机交互)
移动设备
钥匙(锁)
数据收集
实时计算
人工智能
基线(sea)
加速度计
活动识别
原始数据
计算机视觉
低功耗蓝牙技术
人机交互
智能手机应用
蓝牙
数据挖掘
运动(物理)
移动计算
心率
作者
Jiayi Shao,Jiaying Ye,Shengyao Liu,Zachary Englhardt,Girish Narayanswamy,Vikram Iyer,Qiuyue
标识
DOI:10.48550/arxiv.2605.17859
摘要
Wearables are widely used for mobile health monitoring, and photoplethysmography (PPG) is a key sensing modality for heart rate and related physiological measurements. However, public in-the-wild PPG datasets remain largely wrist-centric or limited to short, controlled studies, constraining research on emerging wearable form factors. We present Multi-site PPG, an in-the-wild physiological dataset collected from four custom-developed unobtrusive wearables: a smart earring, ring, watch, and necklace. Each device records green and infrared reflective PPG, 3-axis acceleration, and temperature with timestamps for cross-device alignment, while a Polar H10 chest strap provides reference electrocardiogram (ECG). Participants wore the devices for multiple days during daytime activities while continuing their normal routines. The dataset contains over 350 hours of raw data and 230-290 hours of modeling-ready 8-second windows per wearable. We benchmark heuristic, supervised, and self-supervised heart-rate estimation methods, showing substantial body-site differences: the best methods achieve mean absolute errors (MAEs) of 2.30 bpm on the earring, 5.13 bpm on the ring, 8.37 bpm on the watch, and 8.68 bpm on the necklace. We further analyze motion effects and evaluate multi-site and PPG-accelerometer fusion, demonstrating the dataset's value for robust physiological sensing across emerging wearable form factors.
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