Emotion Recognition in Elderly Based on SpO₂ and Pulse Rate Signals Using Support Vector Machine

Emotion recognition based on physiological signal has become an important issue among researchers nowadays. It is because many studies have proven that emotion condition, especially in elderly, has influenced the physical condition significantly. Nevertheless, there are still few studies which discuss and explores emotion recognition based on SpO₂ and Pulse Rate Signals. This paper proposed emotion recognition of three basic emotions of elders, such as happy, sad and angry based on those physiological signals. Window size segmentation that was used to extract both physiological signals was 15 second. Then, statistical feature extraction method was used to obtain the features of SpO₂ and Pulse Rate (PR). Support Vector Machine (SVM) with selecting the best of C and γ parameters and the most optimal K parameters of k-Nearest Neighbors (k-NN) method were used to classify the extracted features which were tested in several scenarios: classification using SpO₂, using PR and using SpO₂-PR features. The result showed that SVM achieved the best accuracy (72.86%) and precision (71.30%) compared to k-NN. Furthermore, combining the features of both physiological signals could improve the accuracy and precision scores more than 3.70% compared to the single physiological signal. This result provides information of emotion recognition in term of SpO₂ and PR signals which can be better detected by combining the features of both physiological signals. Moreover, the optimal C and γ parameters of SVM and K-value of k-NN can be implemented to achieve better classification result.