Personalized machine learning classifiers using meta-information and personality traits were more efficient and accurate for continuous pain intensity estimation than classifiers trained on complete data.
Personalized machine learning models using bio-physiological channels can improve the accuracy and efficiency of continuous pain intensity assessment.
In this work, we present methods for the personalization of a system for the continuous estimation of pain intensity from bio-physiological channels. We investigate various ways to estimate the similarity of persons and to retrieve the most informative ones using meta-information, personality traits, and machine learning techniques. Given this information, specialized classifiers can be created that are both, more efficient in terms of complexity and training times and also more accurate than classifiers trained on the complete data. To capture the most information in the different bio-physiological channels, we cover a broad spectrum of different feature extraction algorithms. Furthermore, we show that the system is capable of running in real-time and discuss issues that arise when dealing with incremental data processing. In extensive experiments we verify the validity of our approach.
Kächele et al. (Mon,) conducted a other in Pain intensity assessment. Personalized machine learning classifiers vs. Classifiers trained on complete data was evaluated on Accuracy and efficiency of pain intensity estimation. Personalized machine learning classifiers using meta-information and personality traits were more efficient and accurate for continuous pain intensity estimation than classifiers trained on complete data.
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