⌚HealthGuard - product writeup
Monitor, Diagnose.
Last updated
Monitor, Diagnose.
Last updated
In the modern era, Artificial Intelligence is reshaping the landscape of health monitoring, offering advanced solutions for early detection and management of health risks. Wearable devices, enhanced by AI, are emerging as essential instruments for enhancing health and saving lives.
HealthGuard is pioneering the AI-driven health revolution. It is an innovative health monitoring application that leverages the capabilities of AI to provide predictive insights, early alerts, and diagnosis for conditions like strokes and heart attacks.
Utilizing EdgeMind's edge machine learning (ML) technology, HealthGuard is an application operating on Android-based smartwatches. This whitepaper provides a detailed overview of HealthGuard's technical implementation, the integration of EdgeMind, and the application's functionalities, making it a valuable resource for developers, health tech enthusiasts, and healthcare professionals alike.
Problem
Every 40 seconds, someone in the U.S. suffers a myocardial infarction (MI). The outcomes after MI depend on the time that elapses before treatment begins.
Shockingly, more than half of individuals who have MI die in an emergency room or before reaching a hospital within an hour of symptom onset.
Early intervention can boost survival rates by up to 50%, emphasizing the urgency of immediate care. Yet, the average time from symptom onset to hospital arrival in the U.S. spans 1.5 to 6 hours.
Heart-related symptoms can be hard to detect. The first diagnostic step for confirming MI is the traditional 12-lead electrocardiogram (ECG), which requires specific equipment and professional training to record and interpret.
Purpose of HealthGuard:
Monitoring: HealthGuard is designed to continuously collect health metrics from inbuilt smartwatch sensors. This proactive approach ensures that users are always informed about their health status, allowing them to take timely actions if any potential health risks are detected.
Diagnosing: Beyond mere monitoring, HealthGuard takes a leap forward by utilizing the capabilities of smartwatches to accurately detect heart failure in nonclinical environments. Traditionally, in a clinical setting, a 12-lead ECG is required to diagnose heart failure, specifically Myocardial Infarction. However, HealthGuard's advanced algorithms and integration with smartwatch sensors offer a convenient and efficient alternative for early detection.
Monitoring product features:
Real-Time Health Monitoring: Continuously collects health metrics from inbuilt smartwatch sensors, including heart rate detection, atrial fibrillation detection, and blood oxygen levels.
On-Device Inference: With EdgeMind's ML technology, all data analysis is conducted directly on the smartwatch, ensuring data privacy and reducing latency.
Early Warning System: Timely alerts about potential health risks, such as atrial fibrillation or heart attacks, allowing users to seek medical intervention promptly.
Data Privacy: All data processing and analysis are done on the device, ensuring user privacy and compliance with global data protection regulations.
Diagnosing product features
Diagnosis: Use the smartwatch ECG to diagnose heart issues without the need to be in a clinical setting.
12-lead ECG alternative: Use smart watches built-in ECG as a substitute for a 12-lead ECG. This requires moving the watch around the body to 12 different points.
Illustration shows the Einthoven triangle and how to use the Apple Watch to record leads I through III.
Photographs show how to position the Apple Watch for obtaining precordial electrocardiograms.
HealthGuard is not just another health app; it's a culmination of cutting-edge technology and medical expertise. Developed with a focus on user-centric design and precision, HealthGuard ensures that users have access to accurate health data right at their fingertips. The application operates on Android-based smartwatches and leverages the power of EdgeMind's edge machine learning (ML) technology to provide real-time insights and alerts.
Smartwatch Technology:
Modern smartwatches come equipped with a range of sensors and technologies that make real-time health monitoring possible. Technologies like photoplethysmography (PPG) and electrocardiogram (ECG) are now standard in many smartwatches, enabling them to detect conditions like atrial fibrillation using PPG light technology. Brands like Apple, Fitbit, Samsung, and Google have incorporated these technologies to offer features such as heart rate detection, atrial fibrillation detection, and blood oxygen level monitoring.
HealthGuard's key strength is the integration of EdgeMind, enabling efficient deployment of ML models onto the smartwatch. The process follows EdgeMind's standard workflow:
Model Development: ML models to predict stroke and heart attack risks are created in a resource-rich environment using Python.
Model Training: These models are trained on relevant health datasets to minimize prediction errors.
Model Validation: After training, models are validated using separate datasets, ensuring accuracy before deployment.
Model Conversion: Validated models are converted into a format compatible with EdgeMind's Runtime Container, optimizing for execution in low-resource environments.
Containerization: The converted model is encapsulated within EdgeMind's Runtime Container, along with necessary data preprocessing and postprocessing scripts.
Deployment: The Runtime Container is deployed to the smartwatch using EdgeMind's platform-specific SDK.
Execution of Model: The ML model executes on the device, providing real-time analysis of sensor data.
Monitoring and Updating: The performance of the deployed model is continuously monitored, with updates or retraining triggered as needed.
Continuous Operation: If the model performs satisfactorily and no new data prompts an update, the model continues operating on the device, delivering real-time health monitoring.
Watches & biometric sensors
Apple: heart rate detection, atrial fibrillation detection, blood oxygen detection
Fitbit: heart rate detection, atrial fibrillation detection, blood oxygen detection
Samsung: heart rate detection, atrial fibrillation detection, blood oxygen detection
Google: heart rate detection, atrial fibrillation detection
Omron: heart rate detection, blood pressure detection
Garmin: heart rate detection, blood oxygen detection
Withings: heart rate detection
Future Developments:
HealthGuard anticipates significant enhancements, including the integration of Electronic Health Records (EHR), expanded predictive models for other health conditions, and compatibility with additional smartwatch models and other wearable devices.
Limitations:
While smartwatches offer a convenient way to monitor health, they are not without limitations. Factors like noise in the signals (artifact) can lead to false readings. Additionally, the accuracy of heart rate detection may vary based on skin pigmentation. Moreover, while some smartwatches can perform ECGs, they are not a replacement for the standard 12-lead ECG used in clinical settings.
Conclusion:
HealthGuard represents a significant stride in the realm of health-tech wearable solutions. By harnessing the capabilities of smartwatches and integrating advanced ML algorithms, HealthGuard offers a promising tool for early detection and prevention of critical health conditions. As wearable technology continues to evolve, solutions like HealthGuard will play a pivotal role in shaping the future of healthcare, making it more accessible and proactive.
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