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:
Transformation blocks take raw data from your organizational datasets and convert the data into a different dataset or files that can be loaded in an Edge Impulse project. You can use transformation blocks to only include certain parts of individual data files, calculate long-running features like a running mean or derivatives, or efficiently generate features with different window lengths. Transformation blocks can be written in any language, and run on the Edge Impulse infrastructure.
In this tutorial we build a Python-based transformation block that loads Parquet files, calculates features from the Parquet file, and then writes a new file back to your dataset. If you haven't done so, go through synchronizing clinical data with a bucket first.
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You'll need:
The Edge Impulse CLI.
If you receive any warnings that's fine. Run edge-impulse-blocks afterwards to verify that the CLI was installed correctly.
The gestures.parquet file which you can use to test the transformation block. This contains some data from the Continuous gestures dataset in Parquet format.
Transformation blocks use Docker containers, a virtualization technique that lets developers package up an application with all dependencies in a single package. If you want to test your blocks locally you'll also need (this is not a requirement):
Docker desktop installed on your machine.
1.1 - Parquet schema
This is the Parquet schema for the gestures.parquet file which we'll transform:
To build a transformation block open a command prompt or terminal window, create a new folder, and run:
This will prompt you to log in, and enter the details for your block. E.g.:
Then, create the following files in this directory:
2.1 - Dockerfile
We're building a Python based transformation block. The Dockerfile describes our base image (Python 3.7.5), our dependencies (in requirements.txt) and which script to run (transform.py).
Note: Do not use a WORKDIR under /home! The /home path will be mounted in by Edge Impulse, making your files inaccessible.
ENTRYPOINT vs RUN / CMD
If you use a different programming language, make sure to use ENTRYPOINT to specify the application to execute, rather than RUN or CMD.
2.2 - requirements.txt
This file describes the dependencies for the block. We'll be using pandas and pyarrow to parse the Parquet file, and numpy to do some calculations.
2.3 - transform.py
This file includes the actual application. Transformation blocks are invoked with three parameters (as command line arguments):
--in-file or --in-directory - A file (if the block operates on a file), or a directory (if the block operates on a data item) from the organizational dataset. In this case the gestures.parquet file.
--out-directory - Directory to write files to.
--hmac-key - You can use this HMAC key to sign the output files. This is not used in this tutorial.
--metadata - Key/value pairs containing the metadata for the data item, plus additional metadata about the data item in the dataItemInfo key. E.g.:
{ "subject": "AAA001", "ei_check": "1", "dataItemInfo": { "id": 101, "dataset": "Human Activity 2022", "bucketName": "edge-impulse-tutorial", "bucketPath": "janjongboom/human_activity/AAA001/", "created": "2022-03-07T09:20:59.772Z", "totalFileCount": 14, "totalFileSize": 6347421 } }
Add the following content. This takes in the Parquet file, groups data by their label, and then calculates the RMS over the X, Y and Z axes of the accelerometer.
2.4 - Building and testing the container
On your local machine
To test the transformation block locally, if you have Python and all dependencies installed, just run:
Docker
You can also build the container locally via Docker, and test the block. The added benefit is that you don't need any dependencies installed on your local computer, and can thus test that you've included everything that's needed for the block. This requires Docker desktop to be installed.
To build the container and test the block, open a command prompt or terminal window and navigate to the source directory. First, build the container:
Then, run the container (make sure gestures.parquet is in the same directory):
Seeing the output
This process has generated a new Parquet file in the out/ directory containing the RMS of the X, Y and Z axes. If you inspect the content of the file (e.g. using parquet-tools) you'll see the output:
Success!
With the block ready we can push it to your organization. Open a command prompt or terminal window, navigate to the folder you created earlier, and run:
This packages up your folder, sends it to Edge Impulse where it'll be built, and finally is added to your organization.
The transformation block is now available in Edge Impulse under Data transformation > Transformation blocks.
If you make any changes to the block, just re-run edge-impulse-blocks push and the block will be updated.
Next, upload the gestures.parquet file, by going to Data > Add data... > Add data item, setting name as 'Gestures', dataset to 'Transform tutorial', and selecting the Parquet file.
This makes the gestures.parquet file available from the Data page.
With the Parquet file in Edge Impulse and the transformation block configured you can now create a new job. Go to Data, and select the Parquet file by setting the filter to dataset = 'Transform tutorial'.
Click the checkbox next to the data item, and select Transform selected (1 file). On the 'Create transformation job' page select 'Import data into Dataset'. Under 'output dataset', select 'Same dataset as source', and under 'Transformation block' select the new transformation block.
Click Start transformation job to start the job. This pulls the data in, starts a transformation job and finally uploads the data back to your dataset. If you have multiple files selected the transformations will also run in parallel.
You can now find the transformed file back in your dataset:
Transformation blocks are a powerful feature which let you set up a data pipeline to turn raw data into actionable machine learning features. It also gives you a reproducible way of transforming many files at once, and is programmable through the Edge Impulse API so you can automatically convert new incoming data. If you're interested in transformation blocks or any of the other enterprise features, let us know!
Updating metadata from a transformation block
You can update the metadata of blocks directly from a transformation block by creating a ei-metadata.json file in the output directory. The metadata is then applied to the new data item automatically when the transform job finishes. The ei-metadata.json file has the following structure:
Some notes:
If action is set to add the metadata keys are added to the data item. If action is set to replace all existing metadata keys are removed.
Environmental variables
Transformation blocks get access to the following environmental variables, which let you authenticate with the Edge Impulse API. This way you don't have to inject these credentials into the block. The variables are:
EI_API_KEY - an API key with 'member' privileges for the organization.
EI_ORGANIZATION_ID - the organization ID that the block runs in.
EI_API_ENDPOINT - the API endpoint (default: https://studio.edgeimpulse.com/v1).
Custom parameters
You can specify custom arguments or parameters to your block by adding a parameters.json file in the root of your block directory. This file describes all arguments for your training pipeline, and is used to render custom UI elements for each parameter. For example, this parameters file:
Renders the following UI when you run the transformation block:
And the options are passed in as command line arguments to your block:
For more information, and all options see Adding parameters to custom blocks.
Organizational datasets contain a powerful query system which lets you explore and slice data. You control the query system through the 'Filter' text box, and you use a language which is very similar to SQL (documentation).
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For example, here are some queries that you can make:
dataset like '%AMS Activity Study%' - returns all items and files from the study.
bucket_name = 'edge-impulse-health-reference-design' AND --labels sitting,walking - returns data whose label is 'sitting' and 'walking, and that is stored in the 'edge-impulse-health-reference-design' bucket.
metadata->ei_check = 0 - return data that have a metadata field 'ei_check' which is '0'.
created > DATE('2022-08-01') - returns all data that was created after Aug 1, 2022.
After you've created a filter, you can select one or more data items, and select Actions...>Download selected to create a ZIP file with the data files. The file count reflects the number of files returned by the filter.
The previous queries all returned all files for a data item. But you can also query files through the same filter. In that case the data item will be returned, but only with the files selected. For example:
file_name LIKE '%.png' - returns all files that end with .png.
If you have an interesting query that you'd like to share with your colleagues, you can just share the URL. The query is already added to it automatically.
These are all the available fields in the query interface:
dataset - Dataset.
bucket_id - Bucket ID.
bucket_name - Bucket name.
bucket_path - Path of the data item within the bucket.
id - Data item ID.
name - Data item name.
total_file_count - Number of files for the data item.
total_file_size - Total size of all files for the data item.
created - When the data item was created.
metadata->key - Any item listed under 'metadata'.
file_name - Name of a file.
file_names - All filenames in the data item, that you can use in conjunction with CONTAINS. E.g. find all items with file X, but not file Y: file_names CONTAINS 'x' AND not file_names CONTAINS 'y'.
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You can optionally show a check mark in the list of data items, and show a check list for data items. This can be used to quickly view which data items are complete (if you need to capture data from multiple sources) or whether items are in the right format.
Checklists look trivial, but are actually very powerful as they give quick insights in dataset issues. Missing these issues until after the study is done can be super expensive.
Checklists are written to ei-metadata.json and are automatically being picked up by the UI.
Checklists are driven by the metadata for a data item. Set the ei_check metadata item to either 0 or 1 to show a check mark in the list. Set an ei_check_KEYNAME metadata item to 0 or 1 to show the item in the check list.
To query for items with or without a check mark, use a filter in the form of:
To make it easy to create these lists on the fly you can set these metadata items directly from a transformation block
For the reference design described and used in the previous pages, the combiner takes in a data item, and writes out:
A checklist, e.g.:
✔ - PPG file present
✔ - Accelerometer file present
✘ - Correlation between Polar/PPG HR is at least 0.5
If the checklist is OK, a combined.parquet file.
A hr.png file with the correlation between HR found from PPG, and HR from the reference device. This is useful for two reasons:
If the correlation is too low we're looking at the wrong file, or data is missing.
Verify if the PPG => HR algorithm actually works.
Building data pipelines is a very useful feature where you can stack several transformation blocks similar to the Data sources pipelines. They can be used in a standalone mode (just execute several transformation jobs in a pipeline), to feed a dataset or to feed a project.
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The examples in the screenshots below shows how to create and use a pipeline to create the 'AMS Activity 2022' dataset.
To create a new pipeline, click on '+Add a new pipeline:
In your organization workspace, go to Custom blocks -> Transformation and select Run job on the job you want to add.
Select Copy as pipeline step and paste it to the configuration json file.
You can then paste the copied step directly to the respected field.
Below, you have an option to feed the data to either a organisation dataset or an Edge Impulse project
By default, your pipeline will run every day. To schedule your pipeline jobs, click on the ⋮ button and select Edit pipeline.
Once the pipeline has successfully finished, it can send an email to the Users to notify.
Once your pipeline is set, you can run it directly from the UI, from external sources or by scheduling the task.
To run your pipeline from Edge Impulse studio, click on the ⋮ button and select Run pipeline now.
To run your pipeline from Edge Impulse studio, click on the ⋮ button and select Run pipeline from code. This will display an overlay with curl, Node.js and Python code samples.
You will need to create an API key to run the pipeline from code.
Another useful feature is to create a webhook to call a URL when the pipeline has ran. It will run a POST request containing the following information:
In this section, we will show how to synchronize research data with a bucket in your organizational dataset. The goal of this step is to gather data from different sources and sort them to obtain a sorted dataset (that we will then validate in the next section).
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The reference design described in the health reference design consists of 10 subjects performing 1.5 - 2 hours of activities in a research lab. Participants have a study ID (e.g. AMS_001) that is used to refer to the participant. For each participant we have 4 CSV files:
accelerometer.csv - data from the wearable end device.
ppg.csv - data from the wearable end device.
polar_h10.csv - reference data from a commercial reference device (Polar H10).
labels.csv - labels of the activity, as recorded by the research lab.
We've mimicked a proper research study, and have split the data up into two locations.
accelerometer.csv / ppg.csv - live in the company data lake in S3. The data lake uses an internal structure with non-human readable IDs for each participant (e.g. 2E93ZX for anonymized data):
polar_h10.csv / labels.csv are uploaded by the research partner to an upload portal. The files are prefixed with the study ID:
To create the mapping between the study ID and the internal data lake ID we use a study master sheet. It contains information about all participants, ID mapping, and metadata. E.g.:
Notes: This master sheet was made using a Google Sheet but can be anything. All data (data lake, portal, output) are hosted in an Edge Impulse S3 bucket but can be stored anywhere (see below).
Data is stored in storage buckets, which can either be hosted by Edge Impulse, or in your own infrastructure. If you choose to host the data yourself your infrastructure should be available through the S3 API, and you are responsible for setting up proper backups. To configure a new storage bucket, head to your organization, choose Data > Buckets, click Add new bucket, and fill in your access credentials. Our solution engineers are also here to help you set up the buckets for you.
With the storage bucket in place you can create your first dataset. Datasets in Edge Impulse have three layers:
The dataset, a larger set of data items, grouped together.
Data item, an item with metadata and files attached.
Data file, the actual files.
No required format for data files
There is no required format for data files. You can upload data in any format, whether it's CSV, Parquet, or a proprietary data format.
There are three ways of uploading data into your organization. You can either:
Upload data directly to the storage bucket (recommended method). In this case use Add data... > Add dataset from bucket and the data will be discovered automatically.
Upload data through the Edge Impulse API.
Upload the files through the Upload Portals.
The sorter is the first step of the research pipeline. It's job is to fetch the data from all locations (here: internal data lake, portal, metadata from study master sheet) and create a research dataset in Edge Impulse. It does this by:
Creating a new structure in S3 like this:
Syncing the S3 folder with a research dataset in your Edge Impulse organization (like AMS Activity Study 2022).
Updating the metadata with the metadata from the master sheet (Age, BMI, etc...).
With the data sorted we then:
Need to verify that the data is correct (see validate your research data)
Combine the data into a single Parquet file. This is essentially the contract we have for our dataset. By settling on a standard format (strong typed, same column names everywhere) this data is now ready to be used for ML, new algorithm development, etc. Because we also add metadata for each file here we're very quickly building up a valuable R&D datastore.
All these steps can be run through different transformation blocks and executed one after the other using data pipelines.
