Health Reference Design

In this section, you will find a health reference design that describes an end-to-end ML workflow for building a wearable health product using Edge Impulse. It covers an activity study in a clinical lab, where data is recorded from the wearable end device (PPG + accelerometer), a reference device (Polar H10 HR monitor), plus labels (e.g. sitting, running, biking). The data is collected and validated, then written to a clinical dataset in an Edge Impulse organization, and finally imported into an Edge Impulse project where we train a classifier.

It handles data coming from multiple sources, data alignment, and a multi-stage pipeline before the data is imported into an Edge Impulse project. We won't cover in detail all the code snippets, our solution engineers can help you set this end-to-end ML workflow.

With this health reference design section, we want to help you understand how to create a full clinical data pipeline by:

• Synchronizing clinical data with a bucket

• Validating clinical data

• Querying clinical data

• Transforming clinical data

• Building data pipelines