As a high school student, Erin Smith has already established herself as a computer science whiz. To kickstart her programming endeavors the summer after her sophomore year, she studied the “Coding for Dummies” books and immediately became captivated. Computer science became her “favorite art form for expression and scientific research.” Once she felt comfortable coding, she sought to share her self-taught knowledge to other girls in her community. She created KC STEMinists, which teaches middle and high school girls how to make websites, apps, and technologies that address societal issues.
Erin is particularly interested in the combination of technology and emotions. “The quantification of emotions has fascinated me ever since I was a child. I have always sought to understand the human experience from an emotional standpoint through the lens of philosophy and psychology. However, emotions from a technological or mathematical vantage point have been severely limited. We are lacking critical knowledge about what it means to be human and experience emotions unless we can gain a holistic understanding; this more complete knowledge will come from merging emotions and technology. It will come from better tools, such as Affdex, that will shape our ability to comprehend and quantify emotions.”
She highlights how Emotion AI plays a critical role in her FacePrint project. “Current Parkinson’s disease diagnostic methods are severely hindered due to lack of validated biomarkers. Further, Parkinson’s patients typically experience a ‘masked face’ years before the onset of traditional motor symptoms. Using facial recognition software (Affectiva’s Affdex) and machine learning algorithms, I was able to discover, quantify, and digitize a series of differences that occur in distinct facial muscle movements in Parkinson’s patients. I found that interactions between muscle movements that are typically associated with emotional responses, such as joy and fear, differed in Parkinson’s disease patients. Emotion AI enables changes that are occurring in Parkinson’s patients’ brains to be captured through distinct, outwardly manifested facial muscle and expression changes.”
Erin predicts that as Emotion AI progresses, it will have even more positive effect. She maps out the three major implications: “I believe emotion AI will transform the way that we communicate. Social media platforms such as Facebook or Instagram will become the embodiment of empathy; they will be able to respond to our real time emotions. Second, I believe emotion AI will become increasingly present in mainstream consumer products. Technologies like Fitbits for emotions will become normalcy. This will lead to the third growth trend, which will be the use of emotion AI in healthcare. This last stage of development will merge the initial stages of development; emotion AI present in social media and consumer products will provide critical insights that will revolutionize the way we view healthcare.”