Stress Detection During Motor Activity: Comparing Neurophysiological Indices in Older Adults

The effects of cognitive stress are complex and multi-dimensional with nuanced neural and physiological representations across our lifespan. Chronic and instantaneous stressors are known to alter both executive function and motor performance - a particularly challenging prospect for older adults. Age, sex, and motor activity are critical yet under-represented dimensions in the domain of stress detection. Through the present work, we explore a subset of these variables and the relevance of brain hemodynamics and heart rate variability (HR/V) as biomarkers of stress in an aging population. We rely on a multimodal, sex-balanced, motor-stress data set (N = 59) and an exhaustive machine learning workflow to operationalize the unique neurophysiological states that form the human stress response. We found that a quadratic discriminant was sufficient to separate the two states across feature, demographic, and activity variables. We report a stress detection accuracy between $78-98\%$78-98% when using models trained independently on each feature-set. However, these models were highly sensitive to sex, and activity differences - with distinct regions, and features implicated in stress recognition. Both HR/V and fNIRS based features were excellent indices of cognitive stress, however neither generalized to a degree beneficial toward operational use. Our observations underscore the importance of task-context, age, and sex as factors in modeling stress detection tools for older adults.