Environmental Sleep Quality Prediction

• Purpose
• Correlation analysis
• Data loader
• Applications
• Configuration
• Operating environment
• Precautions
• Reference

Purpose

This information asset analyzes and predicts sleep quality based on data such as the amount of activity, air environment (temperature, humidity, etc.), and sleep conditions obtained from various sensors.

Correlation analysis

A correlation analysis of activity and air quality data is performed to calculate the degree to which the two characteristics tend to vary and change using Pearson correlation^[1]
Generate a correlation matrix between activity and air quality and sleep quality, extract feature values for activity and air quality, and predict feature values for sleep quality.
In addition, weights are assigned to each feature for the prediction of sleep quality and calculated using a regression tree algorithm.
Using the features obtained from the above calculations as input, a prediction model is generated using the Random Forest^[2]algorithm.

Data loader

Data on activity levels, air quality, and sleep conditions used in environmental sleep quality prediction are collected in advance, and data for analysis in the form of event data is extracted and registered in a database.

Applications

• Applications for measuring and predicting the impact of changes in activity and air quality on sleep quality etc.

Configuration

This information asset consists of.

• Processing program (Python language)
  • Correlation Analysis Program
  • Environmental Sleep Quality Prediction Program
• Data model
  • Atmospheric data table (event format)
  • Sleep analysis data table (event format)
  • Sleep data table (event format)
  • Environmental Sleep Quality Data Table (event format)
• prediction model
  • Random Forest^[2]

Operating environment

We confirmed the operation in the following environment.

• OS : Ubuntu 18.04 LTS
• RAM : 4GB Recommended
• DISK SPACE : 100GB or more
• Internet connection available
• Python : 3.x（Does not work with 2.x）
  • Python library
  • psycopg2
  • configparser
  • geojson
  • requests
• PostgreSQL : 9.2 or more

When using the xDataEdge^[3]environment, the following conditions are required in addition to the above.

• Services such as Apache, Nginx, etc. that have HTTP server functions (LISTEN to HTTP/HTTPS ports) must not be running on the OS.
• Have root privileges on the OS where xData Edge is installed

Precautions

• Restrictions
  • Functional Limitations
    • This information asset is not guaranteed to operate in environments other than those shown in the operating environment.
  • Data Restrictions
    • There are restrictions on the data to be registered in the database. For details, please refer to the Data Loader API Manual^[4].
• Disclaimer
  • While every effort has been made to ensure that the information on this page is as accurate as possible, we do not guarantee its accuracy or safety.
  • We are not responsible for any damages caused by the contents of this page or by what is provided.
  • All information provided on this page is sample only and we are not responsible for any predicted results.

Reference

• [1]Israel Cohen, Yiteng Huang, Jingdong Chen, Jacob Benesty(2009): "Noise reduction in speech processing". pp 1–4
• [2]Breiman L (2001) Random forests. Mach Learn 45:5–32
• [3]Data Loader API manual:https://www.xdata.nict.jp/docs/DataLoader/0.6/
• [4]xData Edge manual:https://www.xdata.nict.jp/docs/Edge/1.0/