PPG signal analysis using timing-related features and SVM or k-NN classifiers achieved an accuracy of approximately 57% in detecting baseline, stress, and amusement affective states.
Can timing-related features from short windowed PPG signals accurately classify an individual's affective state?
PPG signal analysis using time-domain features and machine learning classifiers can detect affective states with moderate accuracy without requiring frequency domain features.
The aim of the study is to verify the possibility of accurately recognizing an individual’s affective state through the measurement of parameters extracted from a single PPG signal. For this, we used the WESAD public database consisting of a set of multidomain physiological signal. Specifically, the PPG was chosen as non-invasive signal, easy to collect and indicative of the cardiovascular system’s response to the subject’s emotional involvement. The considered affective state consisted of three conditions: baseline, stress state and amusement state. 17 timing-related features were extracted from the windowed PPG signal and a SVM and a k-NN classifiers were used to predict the affective state of the subject. This study proved the efficacy of the features extraction algorithms related to the time domain, reaching an accuracy of approximately 57% on the test data for both the classifiers. Our work highlighted that the PPG signal analysis combined with appropriate features extraction methods and classification models can lead to good results in detecting the affective state of a subject without considering the frequency domain features.
Fruet et al. (Tue,) conducted a other in Affective state. PPG signal analysis with timing-related features was evaluated on Affective state prediction accuracy. PPG signal analysis using timing-related features and SVM or k-NN classifiers achieved an accuracy of approximately 57% in detecting baseline, stress, and amusement affective states.
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