Date of Award

Spring 6-3-2023

Document Type


Degree Name

Master of Industrial Design


Industrial Design

First Advisor

Leslie Fontana

Second Advisor

Michael Lye

Third Advisor

Marissa Gray


">">Pain perception is a subjective experience that differs significantly among individuals, often leading to inconsistencies in assessment and management. A critical issue within this context is the gender bias in pain evaluation, which contributes to unequal treatment and perpetuates gender inequality within the healthcare system. This thesis presents an in-depth investigation of the problem and proposes the development of a wearable device for objective pain assessment. Physiological parameters — Electrocardiography (ECG) can be collected from cardiac sound signals auscultated by fabrics via nanometre-scale vibrations. Machine learning methods can accurately classify heart rate and acute pain intensity of participants. The device aims to provide an accurate and unbiased measure of pain intensity. The study seeks to validate the device's accuracy and explore potential gender-based differences in pain perception. The overarching goal of this research is to address the gender gap in pain evaluation, ultimately enhancing the quality of pain care for patients across various medical and research settings.



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