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The multi-label feature brings considerable value by preserving the context of longer data samples, simplifying data preparation, and enabling more efficient and effective data analysis. The first improvement is in the way you can analyze and process complex datasets, especially for applications where context and continuity are crucial. With this feature, you can maintain the integrity of longer-duration samples, such as hour-long exercise sessions or night-long sleep studies, without the need to segment these into smaller fragments every time there is a change in activity. This holistic view not only preserves the context but also provides a richer data set for analysis. Then, the ability to select window sizes directly in Edge Impulse addresses a common pain point - data duplication. Without the multi-label feature, you need to pre-process data, either externally or using transformation jobs, creating multiple copies of the same data with different window sizes to determine the optimal configuration. This process is not only time-consuming but also prone to errors and inefficiencies. With multi-label samples, adjusting the window size becomes a simple parameter change in the “Impulse design”, streamlining the process significantly. This flexibility saves time, reduces the risk of errors, and allows for more dynamic experimentation with data, leading to potentially more accurate and insightful models.

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Detecting key events in multi-label samples

When working with time-series data labeled using the multi-label format, detecting short-duration or critical events (e.g. tamper detection, fall detection or short noises among others) is possible. Edge Impulse provides flexible strategies for assigning labels to windows of data during training and inference to ensure the events represented. During the data acquisition process, it’s important to understand the available labeling strategies. Choosing the right approach for handling multi-label events ensure accurate detections. The labeling strategies are selected when designing your impulse in the Create Impulse screen. You need to define your appropriate strategy to detect multi-label events in your data.

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Choosing your window labeling strategy

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Use label at the end of the window

This strategy assigns the label that is active at the end of each window as the label for the entire window. It works well for scenarios where the primary interest lies in the resulting state or activity of the window such as recognizing sustained motions or transitions. If a sample transitions from idle to running within a window, and the last timestamp in the window corresponds to running, the window will be labeled as running.

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Use label X if anywhere present in the window

This strategy assigns a label to the window if a specific event label is present anywhere within the windows’ duration (e.g. tamper, fall, etc). It is particularly useful for detecting short or sparse events that may not occupy the full window but are critical to capture when they occur. With this option, you can configure which label(s) to prioritize. If the selected label is found within any part of the window, the window will be assigned to that label, even if the short event occurs alongside other labels.
This approach ensures better coverage for rare or time-sensitive events and improves the model sensitivity to important transitions or anomalies. If multiple selected labels appear within the same window, the label with the highest number of occurrences is assigned to that window. If none of the selected labels are found, Edge Impulse defaults to using the label at the end of the window.

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Use label X if at least Y% of window

This strategy is useful when an event should occupy a meaningful portion of the window before it overrides the surrounding context. You choose the label or labels to monitor, then set a minimum percentage threshold for how much of the window one of those labels must cover. For example, if you monitor fall with a threshold of 30%, a window is only labeled as fall if at least 30% of the window duration is labeled fall. This helps avoid assigning the event label to windows where it only appears as a very short blip. Use this mode when short label flickers are common, or when a rare event should be present long enough to be considered meaningful for training.

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Use majority label in window

This strategy assigns the label that occupies the largest portion of the window. It is useful when you want each window to represent the dominant activity rather than the final state or a prioritized short event. For example, if most of a window is labeled walking and the final part is labeled standing, the window is labeled walking as long as walking covers the largest share of the samples in that window. Use this mode when labels can change within a window but the dominant label is the most meaningful training target.

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Uploading multi-label samples

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With the CSV Wizard

If your dataset is in the CSV format and contains a label column, the CSV Wizard is probably the easiest method to import your multi-label data. For example: 
seconds_elapsed,    accX,   accY,    accZ,    label

0.00,               0.14642,-0.01645,-0.00858,idle
0.16,               0.15051,-0.01149,-0.00345,idle
0.32,               0.15546,-0.02141,-0.00342,idle
...
20.48,              0.14347,-0.03758,-0.00369,running
20.56,              0.13447,-0.01657,-0.01520,running
20.72,              0.11453,-0.00961,-0.01021,running
Once your CSV Wizard is configured, you can use the Studio Uploader, the CLI Uploader or the Ingestion API:

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With the Edge Impulse info.labels description file

The other way is to create an info.labels file for your dataset. Edge Impulse will automatically detect this file when you upload your dataset and will use it to set the labels. The info.labels file is a JSON file that contains the metadata of your dataset, including the labels. See the File example section in the Edge Impulse labels documentation for more details on the file structure. Ensure you click on the “multi-label” tab when viewing the file examples.
Tip: you can export a public project dataset that uses the multi-label feature to understand how the info.labels is structured.Check the Additional resources section for an example multi-label public project.
Once you have your info.labels file available, to upload it, you can either use the Studio uploader or the CLI uploader.

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Using the Studio uploader

The Studio Uploader will automatically detect the info.labels file:

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Using the CLI Uploader

> edge-impulse-uploader * --info-file info.labels

Edge Impulse uploader v1.23.0
Endpoints:
    API:         https://studio.edgeimpulse.com
    Ingestion:   https://ingestion.edgeimpulse.com

Upload configuration:
    Label:       Not set, will be inferred from file name
    Category:    training
    Project:     Example Multi-label upload (ID: XXXXX)

[ 1/11] Uploading training/machine_multilabel_8.json OK (1589 ms)
[ 2/11] Uploading testing/machine_multilabel_3.json OK (2024 ms)
[ 3/11] Uploading training/machine_multilabel_6.json OK (2176 ms)
[ 4/11] Uploading training/machine_multilabel_2.json OK (2224 ms)
[ 5/11] Uploading testing/machine_multilabel_1.json OK (2394 ms)
[ 6/11] Uploading training/machine_multilabel_8.json OK (2395 ms)
[ 7/11] Uploading training/machine_multilabel_9.json OK (2485 ms)
[ 8/11] Uploading training/machine_multilabel_7.json OK (2603 ms)
[ 9/11] Uploading testing/machine_multilabel_4.json OK (2617 ms)
[10/11] Uploading training/machine_multilabel_11.json OK (3426 ms)
[11/11] Uploading training/machine_multilabel_10.json OK (3488 ms)

Done. Files uploaded successful: 11. Files that failed to upload: 0.

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With the Edge Impulse structured_labels.labels description file

If you want to use the Ingestion API, you will need to use the structured_labels.labels format. The structured_labels.labels file is a JSON file that contains the metadata of your dataset, including the labels. See the File example section in the Edge Impulse structured labels documentation for more details on the file structure. After creating a structured labels file, you can run the following command: 
curl -X POST \
    -H "x-api-key: $EI_PROJECT_API_KEY" \
    -H "Content-Type: multipart/form-data" \
    -F "data=@updown.3.json" \
    -F "data=@structured_labels.labels" \
    https://ingestion.edgeimpulse.com/api/training/files
You can have a look at this tutorial for a better understanding: Ingest multi-label data with Edge Impulse API.

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Visualizing multi-label samples

Please note that you can also hide the sensors in the graph:

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Editing multi-label samples

To edit the labels using the UI, click ⋮ -> Edit labels. The following model will appear:
Please note that you will need to provide continuous and non-overlapping labels for the full length of your data sample. The format is the like following: 
[
    {
        "label": "label 1",
        "startMs": 0,
        "endMs": 2000
    },
    {
        "label": "label 2",
        "startMs": 2001,
        "endMs": 4000
    },
    {
        "label": "label 3",
        "startMs": 4001,
        "endMs": 4500
    }
]

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Classifying multi-label data

In the Live classification tab, you can classify your multi-label test samples:

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Limitations

  • Labeling UI is available but is only text-based.
  • Overlapping labels are not supported
  • The entire data sample needs to have a label, you cannot leave parts unlabeled.
Please, leave us a note on the forum or feedback using the ”?” widget (bottom-right corner) if you see a need or an issue. This can help us prioritize the development or improvement of the features.

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Additional resources