Leveraging pervasive data to study and support mother-infant dyads in the wild

Yao, Xuewen
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The ubiquity of mobile devices and wearable sensors coupled with fast-evolving machine learning algorithms has transformed people's daily life, specifically in the healthcare domain. These advancements can be leveraged to not only detect and infer people's behavior patterns with great precision but also provide "just-in-time" support in a seamless, non-intrusive, and cost-effective fashion.

The first year of a child's life is a particularly challenging period for the mother, and also a vital period for child developments. I hypothesize that pervasive data, such as motion, audio, text data, collected online or in people’s daily life, can be leveraged to provide support to postpartum women and their families in need in the wild.

In my dissertation, I developed models that can detect two clinically-relevant parent and infant behaviors in naturalistic home interactions, namely, infant crying and parent holding. Traditional methods by developmental scientists rely heavily on behavioral observations and self-reported data while computer scientists build models using data collected in controlled environments, such as lab and hospital. These methods limit researchers’ understanding of the natural variation in mother-infant interactions across families, and its specific impacts on child development. In my work, I leveraged data collected in longitudinal home environments and built detection models that provide objective, unobtrusive, and continuous measurements of parent holding and infant crying with accuracy 0.870 and 0.613 respectively. Additionally, I evaluated both models based on assessment scenarios specific to developmental science such as event-based accuracy and contingency analysis.

Another piece of my work focuses on using natural language processing to understand the experience of postpartum women experiencing or at risk of postpartum mood and anxiety disorders and to provide them with empathy in the form of a conversational agent, chatbot. Specifically, I collaborated with Postpartum Support International (PSI) and obtained text transcripts between trained volunteers and support seekers. After analyzing 7014 conversations using a combination of human annotations, dictionary models and unsupervised techniques, I find stark differences between the experiences of "distressed" and "healthy" mothers in psychological states, concerns, and goals. Additionally, incorporating the insights from the descriptive analysis as well as empathy and open questions, I designed, built, and evaluated three chatbots that accept open-ended user input to provide postpartum women with support.