Answers.Body: <p>There could be multiple possible ways to extract the features. One would be to use RNNs for a temporal relationship as the input data is time-series.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-02T10:48:03.590Z
Answers.LastActivityDate: 2020-09-02T10:48:03.590Z
Answers.UserDisplayName: Karan Dhingra
Body: <p>I have IMU (Inertial Measurment Unit- 6 axis) sensor data. The sensor attached on a car and 7 different drivers wipe on same path. I want to extract features and classify drivers. Which type of feature extractor do you guys suggest? I am planning to use PCA and Autoencoders but what do you think about classical signal properties to classify drivers?</p>
  
Tags: <classification><autoencoders><time-series><feature-extraction><signal-processing>
Title: Which type of feature extractor do you suggest to classify sensor data?
UserDisplayName: dasmehdix
ThreadText: # Which type of feature extractor do you suggest to classify sensor data?
  
  (Asked by: username_0 on 2020-09-01)
  
  I have IMU (Inertial Measurment Unit- 6 axis) sensor data. The sensor attached on a car and 7 different drivers wipe on same path. I want to extract features and classify drivers. Which type of feature extractor do you guys suggest? I am planning to use PCA and Autoencoders but what do you think about classical signal properties to classify drivers?
  
  **Tags:** classification, autoencoders, time-series, feature-extraction, signal-processing
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-09-02. Score: 1 | Accepted answer
  
  There could be multiple possible ways to extract the features. One would be to use RNNs for a temporal relationship as the input data is time-series.
  
  
  ---
  
  