We are excited to share our latest findings in predicting finger movement and pressure using machine learning. The results show that our model is capable of predicting the finger movement within a Mean Absolute Error (MAE) of 25, which is a sufficient level of accuracy for detecting both the finger movement and the pressure applied.

These screenshots showcase a portion of the data file available for download, which contains the actual and predicted finger movement and pressure values. Our model not only indicates that a finger is moving but also estimates the amount of pressure being applied, providing valuable insights into the intricacies of finger movements.

This achievement opens up new possibilities for applications that require precise finger movement and pressure detection, such as in rehabilitation therapy, robotics, and gesture-based user interfaces.

We invite you to download the full data file and explore the results in more detail. As we continue to refine our model and improve its accuracy, we look forward to discovering new ways to utilize this technology for the betterment of various fields and industries.

All data to train the model and code available on our Github: https://github.com/turfptax/openmuscle