Building an AI Hammer: Complete Neuton TinyML Pipeline from Data to Deployment

In this technical demonstration, we showcase the complete IoT Edge ML pipeline using Neuton AI’s TinyML platform, now part of Nordic Semiconductor. Watch as we transform an ordinary hammer into an “AI hammer” capable of identifying different materials, demonstrating the full journey from data collection to real-time inference.

The Demo: An AI Hammer

Our demonstration device consists of a custom BLE board based on a FANSTEL module for the Nordic nRF54H20, equipped with an IMU sensor that captures acceleration and gyroscope data across three axes. Using this sensor data, the system learns to identify what material the hammer is striking (cloth, wood, or plastic) with remarkable accuracy.

Complete ML Pipeline Demonstration

This demo walks through the entire machine learning workflow:

1. Data Collection

Using a custom mobile application, we collect real-time sensor data while hammering different materials. The IMU captures the unique vibration signatures of each material type, streaming acceleration and gyroscope readings directly to the mobile device.

2. Training

After exporting the collected samples as CSV files, we upload the dataset to the Neuton AI platform. The training process completes in approximately three minutes, generating a highly optimized C library ready for embedded deployment.

3. Deployment

The trained model is integrated into the device firmware through a simple over-the-air update, transforming the hammer into an intelligent sensor capable of material classification.

4. Real-Time Inference

With the model deployed, the AI hammer achieves 99%+ accuracy in identifying materials in real-time. The mobile application displays live inference results, showing confidence levels for each classification.

Multi-Platform Portability

One of the most compelling aspects of this demonstration is the seamless portability across Nordic’s platform ecosystem. We show the same firmware running on three different connectivity solutions:

Bluetooth LE: Custom nRF54H20-based board for local mobile connectivity
Wi-Fi: nRF7002DK transmitting sensor data over local networks
Cellular IoT: nRF9151DK using LTE-M for global connectivity

All three platforms run identical code, demonstrating the power of Neuton AI’s optimization and Nordic’s unified development environment. The demo even shows simultaneous data collection from both Wi-Fi and cellular devices, highlighting the flexibility of the implementation.

Technical Highlights

Rapid Development: Complete ML pipeline from data collection to deployment in minutes
Edge Processing: Inference runs entirely on-device with no cloud dependency
Resource Efficient: TinyML model optimized for constrained embedded systems
Connectivity Agnostic: Same firmware across BLE, Wi-Fi, and cellular platforms
Real-Time Performance: High-confidence inference with minimal latency

Why This Matters

This demonstration illustrates how accessible machine learning at the edge has become. The combination of Neuton AI’s automated ML pipeline and Nordic’s comprehensive platform support enables developers to add intelligent sensing capabilities to IoT devices without requiring deep ML expertise or extensive computational resources.

Whether you’re building industrial monitoring systems, predictive maintenance solutions, or innovative consumer products, this workflow shows that sophisticated edge AI is within reach, from proof of concept to production deployment.

Ready to explore Edge ML with Neuton AI for your next IoT project? Croxel has extensive experience implementing ML solutions on resource-constrained embedded systems across Nordic’s platform ecosystem. We can help you navigate the complete pipeline from data collection strategy through production deployment.