Edge AI for Industrial and Wearable Devices: How Edge Impulse Makes Machine Learning Easy

Not all heroes wear capes—some wear wearables. And some power conveyor belts. Whatever your application, if you’re working with sensor data and trying to extract meaningful insight at the edge, Edge AI for industrial and wearable devices is now more accessible than ever, thanks to Edge Impulse.

We caught up with Brandon Shiy, who leads the pre-sales solutions team at Edge Impulse, to get a grounded perspective on how real-world engineers can move from tentative curiosity to deployable machine learning—without the PhD.

From Curiosity to Capability: What Edge Impulse Offers

Edge Impulse is an end-to-end edge AI platform that allows engineers to:

• Ingest and label sensor data
• Pre-process and extract features
• Train, validate, and optimise models
• Deploy directly to edge devices, from powerful industrial nodes to battery-operated wearables

It’s all wrapped in an intuitive web-based interface, complete with walkthroughs, visual pipeline editors, and customisable low-code modules for advanced users.

Edge AI in the Factory: Industrial Applications

In the industrial world, use cases typically revolve around computer vision and predictive maintenance. Think conveyor belt inspections, part recognition, defect detection, or anomaly spotting in equipment vibration. The key here is access to consistent power and connectivity—great for processing large volumes of data in real-time.

With Edge Impulse, engineers can begin with a few meaningful samples instead of waiting for months of data collection. This “start small, iterate fast” approach is perfect for industrial projects, where synthetic data, augmentation, and anomaly detection models can kickstart the AI journey even when rare failures are hard to capture.

At the Other End: Low-Power Wearables

Now imagine you’re working on a health or fitness wearable. Your device might not be always connected, and power is at a premium. Edge Impulse helps here too by enabling on-device intelligence that’s as close to the sensor as possible.

Rather than sending gigabytes of raw data to the cloud, smart signal processing and efficient models reduce data volume and energy usage right at the source. You get rapid insights without draining the battery or breaching user privacy.

Same Platform, Vastly Different Use Cases

The real beauty of Edge Impulse lies in its adaptability. Whether you’re building for an industrial plant or a wristband, the same platform supports your entire ML pipeline. It even supports synthetic data generation, automatic labelling, and cross-platform hardware support with most mainstream silicon vendors.

As Brandon puts it: “Just start with some data. You don’t need six months’ worth. You can improve it as you go.”

So, whether you’re wrangling motors or monitoring heart rates, Edge Impulse gives you the tools to embed intelligence where it matters most—on the edge. Check it out at studio.edgeimpulse.com, and bring your data to life.