Patient monitoring, clinical waveforms, medical wearables and Internet of Medical Things (IoMT) network traces for health informatics and machine learning research.
Data from smartphone-based inertial sensors (Accelerometer/Gyroscope) used to classify human activities like walking, climbing, and laying.
Comprehensive Fall Detection dataset combining physiological data (Heart Rate, BP, EEG) with sensor readings to classify fall events.
Clinical dataset used to predict heart disease presence based on physiological markers like Chest Pain, BP, and Cholesterol.
Real traffic data from 9 commercial IoT devices infected by Mirai and BASHLITE botnets. Used for developing anomaly detection systems.
IoT sensor data simulating a blockchain-enabled supply chain, tracking temperature, vibration, and location for logistics optimization.
Inertial sensor data (Accelerometer/Gyroscope) for detecting human activities like walking, sitting, and standing for health monitoring.
The world-famous Cleveland dataset for heart disease classification. Contains 303 instances with 14 key clinical attributes like chest pain type and resting blood pressure.