Answers.Body: <p>It all depends on the kind of data you have and the type of output you expect.</p>
  <h3>Input:</h3>
  <p>For example, if you have a series of numbers (heart rate at every minute, or accelerometer data at every second), you can use some time-series approach, like LSTM.</p>
  <h3>Output / Label</h3>
  <p>If you want to classify a given segment, you should look for <strong>classifiers</strong>.</p>
  <blockquote>
  <p>What Bob was doing from 1PM to 2PM?</p>
  </blockquote>
  <p>If you want to find and extract a sub-sample inside a longer period, that's a very different approach.</p>
  <blockquote>
  <p>Find the start and finish time for all running sessions Bob took last week.</p>
  </blockquote>
  <p>In any case, keep in mind the expected prediction format should match the training data format. And some data are more easy to find.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-29T20:49:15.950Z
Answers.LastActivityDate: 2021-08-29T20:49:15.950Z
Answers.UserDisplayName: Andre Goulart
Body: <p>Let's say that you want to detect if a man is running, walking, or dancing instead of just detecting a man still. What type of neural networks will you use for this purpose?</p>
  
Tags: <neural-networks><long-short-term-memory><model-request><action-recognition>
Title: What type of neural network do you need if you want to detect an action or dynamic pattern instead of a static pattern?
UserDisplayName: S. Feunmajer
ThreadText: # What type of neural network do you need if you want to detect an action or dynamic pattern instead of a static pattern?
  
  (Asked by: username_0 on 2021-08-29)
  
  Let's say that you want to detect if a man is running, walking, or dancing instead of just detecting a man still. What type of neural networks will you use for this purpose?
  
  **Tags:** neural-networks, long-short-term-memory, model-request, action-recognition
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-08-29. Score: 0 | Accepted answer
  
  It all depends on the kind of data you have and the type of output you expect.
  
  ### Input:
  
  For example, if you have a series of numbers (heart rate at every minute, or accelerometer data at every second), you can use some time-series approach, like LSTM.
  
  ### Output / Label
  
  If you want to classify a given segment, you should look for **classifiers**.
  
  > What Bob was doing from 1PM to 2PM?
  
  If you want to find and extract a sub-sample inside a longer period, that's a very different approach.
  
  > Find the start and finish time for all running sessions Bob took last week.
  
  In any case, keep in mind the expected prediction format should match the training data format. And some data are more easy to find.
  
  
  ---
  
  