Key result
Random forest classifier achieves ~98% accuracy recognizing human emotions from non-contact ECG.
Why the study?
Systems capable of assessing human emotional states through physiological signals are needed to evaluate mental state and provide preventive care before conditions progress to mental illness or depression.
Absolute Event Rate: 98.2% vs 86.6%
A machine learning-based approach utilizing non-contact ECG signals can accurately classify distinct human emotional states, highlighting the potential for advanced human-machine interaction systems.
Should not inform clinical decisions; leaves open validation of non-contact ECG emotion classifiers in cardiovascular settings.
Emotion recognition becomes an important aspect in the development of human-machine interaction (HMI) systems. Positive emotions impact our lives positively, whereas negative emotions may cause a reduction in productivity. Emotionally intelligent systems such as chatbots and artificially intelligent assistant modules help make our daily life routines effortless. Moreover, a system which is capable of assessing the human emotional state would be very helpful to assess the mental state of a person. Hence, preventive care could be offered before it becomes a mental illness or slides into a state of depression. Researchers have always been curious to find out if a machine could assess human emotions precisely. In this work, a unimodal emotion classifier system in which one of the physiological signals, an electrocardiogram (ECG) signal, has been used is proposed to classify human emotions. The ECG signal was acquired using a capacitive sensor-based non-contact ECG belt system. The machine-learning-based classifiers developed in this work are SVM and random forest with 10-fold cross-validation on three different sets of ECG data acquired for 45 subjects (15 subjects in each age group). The minimum classification accuracies achieved with SVM and RF emotion classifier models are 86.6% and 98.2%, respectively.
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Alam et al. (2023) studied Healthy volunteers (n=45). Random forest classifier vs. Support Vector Machine (SVM) classifier was evaluated on Minimum classification accuracy of human emotions. A unimodal emotion classifier system using a random forest algorithm on non-contact ECG signals achieved a minimum classification accuracy of 98.2% for recognizing human emotions.
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