The Effect of Active break on Autonomic Nervous Pattern Recognition of Learning States

Abstract Purpose: This paper explores the effect of active break on the distinguishability of autonomic nervous patterns of learning states through machine learning. Method: First, we collected electrocardiogram (ECG) data of 77 subjects before and after active break, and accurately located the R-wave peaks from the ECG signal to calculate the RR interval series. Second, the RR interval samples were segmented according to the inclusion criteria of certain learning states. The initial 39 ECG features were empirically calculated, and the optimal feature combination for learning states recognition was selected through sequential backward selection and leave-one-subject-out cross test. Finally, we established binary-classification models of pairs of learning states and compared their performance of learning states recognition before and after active break. Discussion: Active break can promote or inhibit the students’ academic performance. Besides statistical analysis shows the stability of the cognitive ability. What’s more active break increases the physiological response to fatigue Conclusion: (1) The autonomic nervous patterns of knowledge input-processing and retrieval-processing and those of cognitive load matching and mismatching in knowledge retrieval processing became less distinguishable with the effect of active break. (2) The autonomic nervous patterns of mental fatigue and no fatigue states became more distinguishable with the effect of active break. (3) Stronger parasympathetic nervous activities make students achieve better academic performance during using new knowledge to solve problem stage.