A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis

Abstract

Relaxation helps to reduce physical, mental, and emotional pressure. Relaxation techniques generally enable a person to obtain calmness and well-being by reducing stress, anxiety, or anger. When a person becomes calm the body reacts physiologically, producing the so-called Relaxation Response (RResp) which affects the organism in a positive manner, no matter if it is during a state of relaxation or in the middle of a stressful period. The goal of this paper is to design a system capable of identifying automatically the RResps of a subject by analyzing a single physiological signal, the galvanic skin response (GSR). To do so, a team composed of psychologists, neurologists, and engineers designed two experiments for inducing RResps in the participants while their GSR signals were being collected. The team analyzed the data and identified three different levels of RResp that can be quantified using only two easily calculated GSR features. Moreover, the use of the surface produced by GSR and its linear approximation is totally novel. Finally, the data were classified using decision tree strategies for each of the experiments and, after seeing that the obtained trees were similar, the team synthesized them in a single classification system. The F1 values obtained by the generalized classifier scored between 0.966 and 1.000 for the data collected in both experiments.

Keywords

• Affective computing
• decision trees
• electrodermal activity (EDA)
• galvanic skin response (GSR)
• machine learning
• relaxation response