A Machine-Learning-Based Touch Orientation Detection Method for Piezoelectric Touch Sensing in Noisy Environment
Touch orientation detection is important for piezoelectric touch panels to stabilize force-voltage responsivities. Current touch orientation estimation techniques utilize machine learning algorithms for orientation classification. However, environmental noise could weaken the data quality which will result in a lowered detection accuracy. To address this issue, in this article, we present a noise robustness technique, in which different levels of noise data are injected into the training data. The performance of “dirty” data trained model exhibits a good performance (average mean absolute error (MAE) of 7.8 degrees) among signal-to-noise ratio (SNR) from 3 dB to 40 dB, indicating that an improved user experience can be obtained.
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A Machine-Learning-Based Touch Orientation Detection Method for Piezoelectric Touch Sensing in Noisy Environment
Semantic Scholar · Engineering · 2021
Abstract
Touch orientation detection is important for piezoelectric touch panels to stabilize force-voltage responsivities. Current touch orientation estimation techniques utilize machine learning algorithms for orientation classification. However, environmental noise could weaken the data quality which will result in a lowered detection accuracy. To address this issue, in this article, we present a noise robustness technique, in which different levels of noise data are injected into the training data. The performance of “dirty” data trained model exhibits a good performance (average mean absolute error (MAE) of 7.8 degrees) among signal-to-noise ratio (SNR) from 3 dB to 40 dB, indicating that an improved user experience can be obtained.