- Eklemek veya çıkarmak istediğiniz kriterleriniz için 'Dahil' / 'Hariç' seçeneğini kullanabilirsiniz. Sorgu satırları birbirine 'VE' bağlacı ile bağlıdır. - İptal tuşuna basarak normal aramaya dönebilirsiniz.
In this paper, we study an unsupervised learning problem where the aim is to cluster the emotional state (excitedness, boredom, or stress) using the biofeedback sensor data while subjects perform tasks under different difficulty levels on the robot assisted rehabilitation system-RehabRoby. The dimension of the training vectors has been reduced by using the Principal Component Analysis (PCA) algorithm after collecting the biofeedback sensor measurements from different subjects under different task difficulty levels to better visualize the sensor data. The reduced dimension vectors are fed into a K-means clustering algorithm. Numerica . . .l results have been given to demonstrate that for each training vector, the emotional state decided by the clustering algorithm is consistent with the subjects declaration of his/her emotional state obtained via surveys after performing the task