Individual skills are powerful, but a finished product is what changes the world. In this capstone, you will build a complete, autonomous AI sensor for the home.
1The Guardian Sensor
The Smart Guardian project aims to solve a critical security problem: detecting domestic emergencies (like glass breaking or smoke alarms) without violating resident privacy. Our architecture uses a Local-First paradigm. Raw audio is processed entirely on the microcontroller, and only a 'High-level event' (e.g., 'Glass Break Detected') is ever transmitted. This requires a highly optimized TinyML model capable of running on an Arm Cortex-M4 processor with less than 256KB of RAM, demonstrating the peak of edge optimization.
Project: Smart_Guardian_v1
Sensors: [Mic, Temp, IMU]
Model: Tiny_CNN_Audio
Logic: LOCAL_ONLY
Status: ARCHITECTURE_LOCKED2The Deployment Stack
To build the Guardian, we integrate three key pillars. First, Feature Engineering: transforming audio streams into MFCC spectrograms in real-time. Second, Inference: using TFLM (TensorFlow Lite for Microcontrollers) to execute a quantized CNN inside a 64KB Tensor Arena. Third, Power Management: implementing deep-sleep duty cycles that keep the system dormant until the audio energy exceeds a safety threshold. This end-to-end integration proves your ability to navigate the complex trade-offs of physical AI development.
if (RMS_Energy > 500):
run_tflm_inference()
if (prediction == GLASS_BREAK):
ble_send_alert('EMERGENCY')
Status: CASCADE_FLOW_ACTIVE