Anticipating Explicit Motor Learning by Assessing Arousal Levels using HRV and GSR

Abstract

Biometrics, including heart rate variability (HRV) and galvanic skin response (GSR), are already used to gauge autonomic regulation, emotional reactivity, attention, and flow, a concentration state. Given the role of arousal seen in motor learning factors such as optimal stress, anxiety, and task engagement, this study investigates whether HRV and GSR show distinguished patterns in those who explicitly learn a hidden sequence in a motor task as compared to those who only learn implicitly. This is done using a serial reaction time task (SRTT) and the collection of electrocardiogram (ECG) and GSR data throughout the task then comparing qualitative data across subjects. HRV decrease and GSR increase are noted at serval instances of explicit motor learning emergence, and even in instances when the shift is not exaggerated, it is never found varying in the opposite direction as the hypothesized pattern. Despite a low participant sample size and a low sampling frequency for ECG and GSR, the results tentatively support the concept of using HRV and GSR to gauge whether or not a person’s current state is conducive to explicit motor learning. This biometric monitoring holds the potential for real-time biofeedback and could be useful in physical rehabilitation settings due to the relative ease of implementation.