I understand you want to use machine learning to detect if a person is awake or asleep by analysing real time breathing recording.
The first step is to collect a dataset of breathing data from people in both awake and asleep states. Once you have collected the dataset, you can use a variety of feature extraction techniques to extract features from the data. These features can then be used to train a machine learning model to classify breathing data as awake or asleep.
Here is a general overview of the steps involved:
- Collect a dataset of breathing data: The dataset should include breathing data from people in both awake and asleep states. The data can be collected using a variety of devices, such as a smartphone, a smartwatch, or a medical device.
- Extract features from the breathing data: There are a variety of feature extraction techniques that can be used to extract features from breathing data.
- Train a machine learning model: Once you have extracted features from the breathing data, you can train a machine learning model to classify the data as awake or asleep. There are a variety of machine learning algorithms that can be used for this task, such as support vector machines, random forests, and neural networks.
- Use the machine learning model to detect if a person is awake or asleep: Once the machine learning model is trained, you can use it to detect if a person is awake or asleep by analysing real-time breathing data.
For further information, refer to the documentation link below:
