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I will develop tinyml firmware for sensor anomaly detection and motion classification
Edge AI TinyML Firmware for ESP32 Smart Sensors and Embedded Products
i develop embedded firmware for intelligent devices that process sensor data locally and respond in real time. My focus includes ESP32 S3 edge AI, TinyML deployment, camera and microphone inference, s...
About this Gig
I develop embedded machine learning solutions for sensor anomaly detection, motion classification, vibration monitoring and predictive maintenance prototypes.
My services include sensor data acquisition, preprocessing, filtering, windowing, feature extraction, model integration and on-device inference. I can help deploy lightweight ML models to supported microcontrollers such as ESP32 and STM32, using suitable tools and frameworks such as Edge Impulse or TensorFlow Lite for Microcontrollers.
Applications include abnormal vibration detection, motor condition monitoring, accelerometer and gyroscope classification, gesture recognition, equipment monitoring and intelligent IoT sensors.
The focus is practical edge AI: running inference locally while considering memory, processing time and power constraints. Depending on your project, deliverables can include documented firmware source code, model integration, build instructions, testing results and resource benchmarks.
Already have a trained model? I can help integrate it into your firmware. Starting with sensor data? I can assess the data and recommend a suitable preprocessing and deployment approach.
FAQ
Can you develop TinyML firmware for ESP32 or STM32?
Yes. I can integrate suitable TinyML models with supported ESP32 or STM32 targets, including sensor acquisition, inference logic and build configuration. Compatibility depends on the specific chip, framework and model requirements.
Can you implement sensor anomaly detection?
Yes. I can help implement anomaly detection for vibration, motion and other time-series sensor data. The method depends on the available dataset, expected abnormal conditions and target hardware.
Can you build accelerometer and gyroscope motion classification?
Yes. I can develop firmware for gesture recognition, movement classification and IMU-based activity detection, including signal preprocessing and integration of a suitable classification model.
Do you support predictive maintenance and vibration monitoring?
Yes. I can develop sensor-based prototypes for abnormal vibration detection, machine condition monitoring and predictive-maintenance experiments. Reliable fault detection requires representative operating data and validation.
Can you train a custom machine learning model?
Potentially, depending on the project scope and data. Please provide sample sensor readings, labels where available, the expected output and target device so I can assess whether custom training is appropriate.
. Can you deploy an existing model using Edge Impulse or TensorFlow Lite Micro?
Yes, provided the model, operators, toolchain and target hardware are compatible. I can help integrate the inference library and connect it to the sensor-processing pipeline.
Will the model run offline on the microcontroller?
Local inference is possible when the model and required operations fit the device's available resources. Cloud connectivity is not inherently required for on-device inference.
Do you provide source code and model performance results?
Deliverables depend on the package. They can include firmware source code, build instructions, integration notes and measured inference latency or memory usage when the target and test environment are available.
