Towards Understanding and Modeling Empathy for Use in Motivational Design Thinking

TL;DR

This paper explores modeling empathy in Design Thinking using physiological signals and proposes an improved approach to foster diverse, outside-the-self thinking during workshops.

Contribution

It introduces a novel method to quantify empathy with Gaussian mixture models and physiological data, enhancing understanding of empathy development in Design Thinking.

Findings

Physiological signals can indicate empathy onset.

Modeling empathy helps improve Design Thinking outcomes.

Enhanced methods promote diverse, outside-the-self thinking.

Abstract

Design Thinking workshops are used by companies to help generate new ideas for technologies and products by engaging subjects in exercises to understand their users' wants and become more empathetic towards their needs. The "aha moment" experienced during these thought-provoking, step outside the yourself activities occurs when a group of persons iterate over several problems and converge upon a solution that will fit seamlessly everyday life. With the increasing use and cost of Design workshops being offered, it is important that technology be developed that can help identify empathy and its onset in humans. This position paper presents an approach to modeling empathy using Gaussian mixture models and heart rate and skin conductance. This paper also presents an updated approach to Design Thinking that helps to ensure participants are thinking outside of their own race's, culture's, or other affiliations' motives.