New Article: Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data

Two timestreams, warped to common timing

As Wear-IT begins to take shape in more detail, we’ve been looking at new ways to understand and model physiological data on an individual level. Recent work with Ame Osotsi, Zita Oravecz, and Joshua Smyth examines individual differences in physiological response in a paper called Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data, just published in the Journal of Healthcare Informatics Research.
In it, we illustrate the importance of individual modeling for physiological states, showing that high-arousal states have physiological signatures that can be detected by machine learners, but that those signatures differ enormously from person to person, and individualized modeling is really important.

GSA 2019

Dr. Brick and Ms. Dickens presented talks at the annual meeting of the Georntological Society of America (GSA 2019) in Austin, Texas this week. GSA is always an interesting meeting, looking at the science of aging–a vital science, especially as the global trends show our population living longer, and the proportion of older adults increasing as time goes on. Topics included methods to model and understand patterns in individual growth trajectories (like Dr. Brick’s sequence learning methods), and the relationships between sleep and well-being.

Six-state emotional day solution

Other topics focused on issues of cognitive aging, dementia and Alzheimers, and technological and methodological innovations to model and improve the well-being of older adults through improved cognitive function, better mobility, and increased independence.

industryXchange 2019

Dr. Brick, Ms. Dickens, and Mr. Mundie all attended Penn State’s industryXchange 2019 to talk about new developments from the lab. This year’s workshop focused on sensors and their applications, so we mostly showed off applications of wearable and passive measurement devices, and the ways that they could be applied to improve health and well-being, and assist with psychopathology.

Dr. Brick also presented some upcoming work with Dr. Jessica Menold using these same approaches to enhance workplace efficiency, reduce worker stress and burnout, and improve on-the-job learning.

Quantitative Methods Series at USC

Dr. Brick took a trip this week to the University of Southern California to visit Dr. Chris Beam in the Psychology department, and Dr. John Prindle in the School of social work.

He also gave a presentation to kick off USC’s Quantitative Methods Series, discussing new models of day-to-day affect and emotion, and their expected relationships to emotion regulation.  Lots of interesting discussion about exciting data sets and interesting new approaches to measurement!

Emotion Over Time

Core affect is a way of thinking about emotional mood state in terms of two measures: valence (from positive to negative) and arousal (from highly active to placid). So if you’re super ticked off about something, that’s negative valence and high arousal, where if you’re just blissfully chill, you’re low arousal and positive valence.   But people aren’t just in one state forever–much more interesting is the way that they change.

Zita Oravecz and I look at the dynamics of core affect change within an individual in a recent paper: Associations Between Slow- and Fast-Timescale Indicators of Emotional Functioning in the journal Social Psychological and Personality Science.  There, we characterize the changes in core affect within a person in terms of a person’s home base in each dimension, the state they drop back to when there’s no real input, how much they fluctuate around that home base, how strongly their system regulates itself back to base, and the correlations between the ways those characteristics show up in valence and arousal.  Importantly, these characteristics turn out to be predictive of other “trait-level” characteristics, like the strategies you use when you have to deal with negative emotion.