Multimodal physiological dataset from 15 subjects wearing chest and wrist sensors. Includes ECG, EDA, EMG, respiration, temperature, and accelerometry. CSV/pickle format. Used for stress detection and affective computing research.
Large-scale quality-assessed ICU PPG benchmark derived from MIMIC-III, with ECG, ABP, and respiration signals in 30-second WFDB segments. Multi-task format supporting cardiovascular and respiratory signal analysis for wearable algorithm development.
MC-MED provides high-resolution multimodal clinical and physiological data from 118,385 adult emergency department visits, supporting real-time monitoring and AI-based medical research.
Multimodal dataset from 24 participants with synchronized wrist-worn PPG from Galaxy Watch 5 and Empatica E4, plus chest ECG from Polar H10, collected during diverse activities in semi-naturalistic settings for evaluating consumer-grade wearable performance.
Large public dataset from Microsoft Research Aurora Project with ECG and PPG signals from wrist-worn wearables, balanced for gender, age, and hypertension status, enabling causal inference research for non-invasive blood pressure prediction with 205 extracted features.
67,830 record sets of multi-channel physiologic waveforms (ECG, ABP, respiration, PPG) and vital sign time series from approximately 30,000 ICU patients, supporting clinical research and ML model development for critical care.
48 half-hour excerpts of two-channel ambulatory ECG recordings from 47 subjects (1975–1979), digitized at 360 Hz with expert annotations for approximately 110,000 heartbeats, widely used for arrhythmia detection research.[page:2][web:219]
Multimodal body motion and vital sign recordings from 10 volunteers performing 12 physical activities, collected with three body-worn sensor units (chest, wrist, ankle) including 2‑lead ECG.[web:124][web:130][web:148][web:153]
Realistic benchmark dataset for Internet of Medical Things (IoMT) security research. Captures biomedical sensor data and network traffic from medical devices including ECG monitors, pulse oximeters, and other healthcare IoT equipment.