Real Time Breathing Data Analysis

So, we want to detect if a person is awake or asleep by analyzing real time breathing recording. The recording will be done by a simple recorder(smartphone/matlab) and our program will try to analyze the breathing cycle from the real time data and once the person is asleep, the program will give an alarm. Now, we are novices in this field and we don't know what toolbox/method is to be used in this case.

At first, we thought that we would use machine learning for this project. So, we recorded various samples of awaken_state and sleeping_state (generally spanning a minute) from different persons. Still we are trying to figure out how to extract features and use those features in real time breathing data processing..

A friendly suggestion would be very much appreciated!

Answer 1

[Vidip Jain]

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:

1. 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.
2. Extract features from the breathing data: There are a variety of feature extraction techniques that can be used to extract features from breathing data.
3. 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.
4. 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:

• https://www.mathworks.com/discovery/feature-extraction.html
• https://www.mathworks.com/help/audio/ref/audiofeatureextractor.html