Classify Movement Data

This class is designed to analyze movement data using a pre-trained model. It takes movement data as input, predicts emotional states (like mood or stress level) based on the data, and then saves the results to a file. It works automatically, so you don't need to know how the model itself works.

Step-by-Step Explanation:

1. Loading the Pre-Trained Model:
   • The class uses a pre-trained machine learning model (a program that has already learned how to make predictions based on past data).
   • When you run this class for the first time, it finds and loads this model from a file.
2. Processing Movement Data:
   • The input data (like movement from a smartwatch) must already be prepared and organized into a table of numbers (called "features").
   • This class uses the loaded model to analyze the movement data and predict emotions or states based on it.
3. Adding Predictions:
   • Once predictions are made, the class adds a new column to your table. This column contains the predicted emotions for each row of data.
4. Saving the Results:
   • The updated table (with the predictions added) is saved as a file. This file is easy to open in programs like Excel or similar tools.
   • The file's name includes the "window length" (a setting for how the data is divided into chunks), making it easy to identify later.
5. Messages:
   • The class shows messages like "Model loaded" and "Data classified successfully" to confirm that everything is working as expected.