Internet of Medical Things IDS dataset with BLE, IP packet, and IP flow captures. CSV and pickle formats, 2.7 GB, for ML-based healthcare IoT intrusion detection.
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.
Wearable IoT dataset with 18 physical activities from 9 subjects wearing 3 IMUs and a heart rate monitor. 54 columns including temperature, acceleration, and gyroscope data. CSV format. Used for HAR, activity classification, and intensity estimation.
Real-world wearable dataset from 15 nurses over one week in a hospital. Contains 11.5 million entries of EDA, heart rate, skin temperature, and orientation data collected via Empatica E4. CSV format. Used for occupational stress detection 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.
A pair of labeled MQTT and UDP DDoS datasets for Healthcare-IoT networks, generated with Cooja and ns-3 simulators to support evaluation of DDoS detection and mitigation techniques in H-IoT environments.
Multi-source healthcare dataset integrating Electronic Health Records, medical imaging (CT and MRI scans), and wearable IoT sensor data for personalized treatment optimization. Includes 5,008 brain imaging files and real-time physiological monitoring data.
IoT-based environmental perception data studying impact on university students' mental health. Integrates temperature, humidity, noise, and air quality sensors.