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| // This header is automatically generated by HEARTFLOW_OS /export_tinyml | |
| // Place the exported model.h file in the src/ directory alongside this main.cpp | |
| void setup() { | |
| Serial.begin(115200); | |
| while (!Serial); | |
| Serial.println("=========================================="); | |
| Serial.println("HEARTFLOW_OS // EDGE INFERENCE NODE ONLINE"); | |
| Serial.println("=========================================="); | |
| // Mock Telemetry Vector | |
| // Format: [Heart_Rate, SpO2_Level, Systolic_BP, Diastolic_BP, Body_Temp] | |
| float features[5] = {75.0, 98.0, 120.0, 80.0, 37.0}; | |
| Serial.println("Feeding telemetry payload to C-compiled Random Forest..."); | |
| unsigned long start_time = micros(); | |
| // The predict() function is defined in model.h | |
| // Returns integer class (e.g., 0 = Healthy, 1 = Heart Disease, etc.) | |
| int prediction = predict(features); | |
| unsigned long end_time = micros(); | |
| Serial.print(">> INFERENCE RESULT: CLASS "); | |
| Serial.println(prediction); | |
| Serial.print(">> INFERENCE TIME: "); | |
| Serial.print(end_time - start_time); | |
| Serial.println(" microseconds"); | |
| } | |
| void loop() { | |
| // In a real environment, read from physical sensors (MAX30102, MLX90614) here | |
| // then pass to predict() and emit via BLE or MQTT. | |
| delay(5000); | |
| } | |