Physiological response to acute stress against confounding factors: a white-box research method

Abstract

Objectives: Physiological sensors appear a promising tool to monitor psychological stress in the daily life. Yet, despite the increasing know-how developed in affective computing, physiology-driven stress detection algorithms still yield unsatisfactory results on the field. This study intends to highlight physiological patterns that are both stress-sensitive and stress-specific, at the multi-subject scale, in several contexts. Approach: A notional framework was developed and empirically validated, which distinguishes psychological stress from two context-related confounding factors: Background and Activity. A physiological database was elaborated from a multi-task experimental protocol in a standardized environment with skin conductance, electrocardiograph and respiration recordings. Stress-induced variations were isolated in standard physiological features, to select the most stress-sensitive ones independently from confounding factors. Main Results: Most standard features were unable to distinguish acute psychological stress from low cognitive activity (e.g. frequency of skin conductance responses, or spectral power in the low and high frequency bands of the interbeat intervals). After sign-rank tests between the stressful and control conditions of two standard tasks involving arithmetic or speech, their effect sizes remained below 0.3. A number of physiological features also showed task-dependency. Yet, promising correlates were highlighted among cardiac features such as mean heart rate and its standard deviation, or the root mean square of its successive differences (effect sizes above 0.3 for both tasks). Significance: The high sensitivity to context observed in standard features is likely to explain the poor validity on the field of physiology-driven models developed in controlled environments. In particular, mental activity should be considered as confounding factor while elaborating these models to increase their accuracy. The small feature set highlighted in this paper and the use of control data should enhance the generalization abilities of any machine learning algorithm; still, further research is needed to evidence new stress-specific physiological responses.