Hybrid System
ALARMS
IoTPredictiveHeart-healthMonitoring
Problem Statement
Healthcare monitoring systems often lack real-time alerting and secure data handling for critical patient data.
System Architecture
01
ESP32
02
MQTT
03
Flask REST APIs
04
ML Inference
05
WebSockets
06
Real-time Alerts
Solution Overview
Created an end-to-end vitals anomaly detection pipeline (heart rate, body temperature, accelerometer) achieving AUC 0.98 and 94% validation accuracy.
End-to-end vitals anomaly detection pipeline with AUC 0.98
94% validation accuracy on health metrics
Real-time ingestion and alerting using MQTT + Flask REST APIs
Continuous sensor streaming from ESP32 with low-latency monitoring
Monitoring UI with live updates via WebSockets
94%
accuracy
0.98
auc
<100ms
latency
Technology Stack
PythonFlaskTensorFlow/KerasMQTTESP32WebSocketsAWSNext.js