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Enhancing Real-Time Predictions with GPT-4 for a Time-Domain Sensor System

In this article, we explore how we utilized ChatGPT, specifically the GPT-4 model, to identify the best artificial neural network library and model for a system with 12 sensor input features and a time-domain dataset, accompanied by a 4-sensor label output with time-domain values. Not only did GPT-4 provide valuable insights, but it also assisted in debugging the code when needed.

Finding the Right Model:

Our primary goal was to find a suitable model that could handle the multivariate time series data from our sensors effectively. With the help of GPT-4, we decided to use scikit-learn, a popular Python library for machine learning. We chose the RandomForestRegressor model wrapped in a MultiOutputRegressor for our specific use case, as it can capture non-linear relationships between input features and output labels.

Training the Model and Evaluating Performance:

We trained our model with a dataset containing 20 minutes of data, which included noise from the Open Muscle system version 5.3.0. Despite the noise, our model achieved a Mean Absolute Error (MAE) of between 25 and 50, with output label ranges from 5200 to 5500. This result roughly translates to an error rate of around 12%, indicating the model’s reasonable performance in capturing the underlying patterns in the data.

Real-Time Predictions with Pickle:

To utilize our trained model for real-time predictions, we saved it using Python’s pickle library. This allowed us to seamlessly load the model into our application and feed it live data from the sensors. As a result, we were able to compare the real output and the predicted output, enabling us to assess our model’s performance in real-time.

Next Steps for Improvement:

Although our current model performs reasonably well, there are several steps we can take to further enhance its performance and the quality of our sensor data:

  1. Improve the signal quality of the Open Muscle system by upgrading from 12-bit to 24-bit ADCs. This will increase the resolution and accuracy of our sensor readings.
  2. Implement proper shielding for the PCBs to reduce electromagnetic interference and noise in the sensor data.
  3. Train the model with a larger dataset to better capture the underlying patterns and relationships in the data.


Using ChatGPT, specifically the GPT-4 model, we successfully identified and implemented an artificial neural network model to handle our time-domain sensor system. With real-time predictions in place, we can now focus on improving the signal quality and training our model with more data to achieve even better performance.

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