# Sleep Detection Task

## Overview

The objective of sleep detection is to identify periods of sustained sleep over the course of several days or weeks. The de facto standard for long-term, ambulatory sleep detection is actigraphy, which is a method of monitoring gross motor activity using an accelerometer. However, actigraphy is not a reliable method for sleep detection as it employs very simple heuristics to determine sleep. Often actigraphy can misclassify periods of inactivity or even not-worn as detected sleep.

In this task, we look to leverage a light-weight model that can outperform actigraphy similarly using only data from an IMU. For more advanced sleep analysis, refer to the [Sleep Stage Classification](https://ambiqai.github.io/sleepkit/tasks/stage/). By leveraring Ambiq's ultra-low-power microcontroller along with an ultra-low-power IMU, an efficient AI enabled actigraphy or fitness band will be able to run for weeks off a single charge.

Wrist-based Sleep Detection

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## Model Zoo

The following table provides the latest performance and accuracy results for pre-trained models. Additional result details can be found in [Model Zoo → Detect](https://ambiqai.github.io/sleepkit/zoo/detect/).

| NAME             | LOCATION | # CLASSES | MODEL | PARAMS | FLOPS   | ACCURACY | AP    |
| ---------------- | -------- | --------- | ----- | ------ | ------- | -------- | ----- |
| __SD-2-TCN-SM__  | Wrist    | 2         | TCN   | 9K     | 1.5M/hr | 95.9%    | 94.5% |

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## Target Classes

Below outlines the classes available for sleep detect classification. When training a model, the number of classes, mapping, and names must be provided.

| CLASS   | LABELS          |
| ------- | --------------- |
| 0       | AWAKE           |
| 1       | SLEEP           |

**Class Mapping**

Below is an example of a class mapping for a 2-class sleep detect model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped.

```json title="JSON example"
{
    "num_classes": 2,
    "class_names": ["AWAKE", "SLEEP"],
    "class_map": {
        "0": 0,
        "1": 1
    }
}
```

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## References

* [AI-Driven sleep staging from actigraphy and heart rate](https://doi.org/10.1371/journal.pone.0285703)