I will develop esp32 s3 tinyml sensor firmware anomaly detection predictive maintenance

Professional ESP32-S3 TinyML sensor firmware for anomaly detection motion classification predictive maintenance nodes and presence sensing. I build efficient low power on device AI solutions using Edge Impulse or TensorFlow Lite Micro with full local inference and no cloud dependency.

Many buyers receive generic Arduino sketches that fail to fit models in limited memory or cause fast battery drain. My optimization targets the ESP32-S3 dual core LX7 PSRAM vector instructions and low power modes achieving sub second inference often under thirty milliamps average with over ninety five percent accuracy in real conditions. I address key challenges including custom dataset guidance in situ feature extraction time frequency domain preprocessing model quantization memory tuning power profiling and integration with sensors like MPU6050 IMU accelerometers vibration sensors

You receive complete end to end service covering requirements analysis sensor selection on device preprocessing custom model optimization full inference pipeline with configurable thresholds local decision logic and smart alerts via GPIO or MQTT summary data only. Thorough testing delivers accuracy benchmarks latency