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Understanding and Assessing the Performance of a Sensor System with Mean Absolute Error

In this article, we discuss the performance of a sensor system with 12 input features and a time-domain dataset, accompanied by a 4-sensor label output with time-domain values. To evaluate the model’s performance, we focus on the Mean Absolute Error (MAE) metric. We’ll explore the significance of the MAE values obtained and the implications for the performance of the sensor system.

Mean Absolute Error (MAE) as a Performance Metric:

Mean Absolute Error is a commonly used metric in regression problems to measure the average absolute difference between the predicted values and the true values. It is calculated as follows:

MAE = (1/n) * Σ|y_true – y_pred|

where n is the number of samples, y_true is the true label, and y_pred is the predicted label. A smaller MAE indicates better model performance, as it means the predicted values are closer to the true values.

Evaluating Model Performance:

Our model achieved an MAE value between 25 and 50, with the output label ranges for the labels being between 5200 and 5500. To better understand the significance of these values, we can calculate the relative MAE:

Relative MAE = MAE / Output Label Range ≈ 25 / (5500 – 5200) ≈ 0.12, or 12%

The relative MAE tells us that, on average, the model’s predictions are off by about 12% of the range of the output labels. This indicates a reasonable performance, given the complexity of the sensor system and the presence of noise in the data.

Improving Model Performance:

There are several ways to further improve the performance of the sensor system and reduce the MAE:

  1. Collect more data: Training the model with a larger dataset can help it better capture the underlying patterns and relationships in the data.
  2. Improve data quality: Reducing noise in the sensor data by implementing proper shielding for the PCBs or upgrading the ADCs can lead to more accurate sensor readings.
  3. Feature engineering: Creating new features or transforming existing ones can improve the model’s ability to capture the relationship between the input features and output labels.
  4. Model optimization: Experiment with different model architectures, hyperparameters, or ensemble methods to find a more suitable model for the specific problem.


The Mean Absolute Error metric provides valuable insights into the performance of our sensor system. With an MAE of 25-50 and a relative MAE of 12%, the current model shows reasonable performance. However, there is room for improvement, which can be achieved by collecting more data, improving data quality, performing feature engineering, and optimizing the model. By following these steps, we can further enhance the performance of our sensor system and reduce the MAE.

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