Healthcare IoT Data - Wearable Devices for Patient Remote Monitoring

Dataset Overview

This dataset simulates sensor data collected from wearable devices in an Internet of Things (IoT)-based healthcare system. The data corresponds to patient health monitoring with sensors that measure various vital signs, providing real-time information that can be transmitted to healthcare providers for remote patient monitoring and early intervention.

Key Features

• Real-time vital sign measurements from wearable IoT devices
• Temperature monitoring for fever detection and body temperature tracking
• Blood pressure measurements (systolic and diastolic)
• Heart rate monitoring with beat-per-minute readings
• Device battery level tracking for maintenance alerts
• Patient identification and timestamped readings
• Suitable for continuous remote patient monitoring scenarios
• Data format designed for healthcare analytics and alert systems

Data Structure

The dataset includes the following health monitoring parameters:

• Patient ID: Unique identifier for each monitored individual
• Timestamp: Date and time of sensor reading
• Body Temperature: Temperature in Celsius or Fahrenheit
• Heart Rate: Beats per minute (BPM)
• Blood Pressure: Systolic and diastolic pressure readings (mmHg)
• Oxygen Saturation (SpO2): Blood oxygen level percentage
• Activity Level: Movement and activity indicators
• Battery Level: Device power status (percentage)
• Alert Flags: Abnormal reading indicators

Data Collection Method

The dataset simulates realistic wearable device sensor readings based on medical device specifications and clinical guidelines. Sensor values are generated to reflect normal physiological ranges as well as anomalous conditions that would trigger healthcare alerts. The simulation accounts for sensor noise, measurement variability, and battery depletion patterns typical of real IoT medical devices.

Research Applications

• Remote patient monitoring system development
• Early warning systems for health deterioration
• Chronic disease management through continuous monitoring
• Elderly care and fall detection applications
• Post-operative patient tracking and recovery monitoring
• Clinical decision support system integration
• Telemedicine and virtual healthcare platforms
• Healthcare IoT device evaluation and benchmarking

Machine Learning Use Cases

• Anomaly detection for abnormal vital sign patterns
• Classification of health conditions based on sensor readings
• Time series forecasting for vital sign predictions
• Clustering patients by health status and risk levels
• Alert prioritization using severity classification models
• Predictive models for patient deterioration
• Feature engineering from multi-sensor vital sign data
• Deep learning for pattern recognition in health data
• Personalized health thresholds through adaptive algorithms