Our lab uses wearable sensors to study how daily activities of mothers and their infants contribute to their development and well-being. We use various sensors (including some similar to a Fitbit) to access our participants’ activity “in the wild” –that is, in day-to-day, real-world settings. Where a Fitbit helps people meet fitness goals by tracking steps, we are interested in learning about development by tracking activities that we believe matter for long-term outcomes. For example, we want to track infants’ mood and sleep patterns over the course of the day, or playing, talking and soothing interactions they have with their caregivers.
In our current study, mothers and their infants use wearable sensors to track their activity over the course of 24-72 hours as they go about their day. Our sensors record various types of data which we will use to infer patterns of behavior. These include arousal data (e.g. heart rate), accelerometer/ motion data (i.e. what Fitbits use to track steps), and audio recordings (to capture patterns of mood and interactions).
Interested students will have the opportunity to gain experience interacting with participants and data collection, and/or analysis of mobile-sensor data streams.
We will work in an interdisciplinary team bridging developmental science, clinical psychology, computer science, human-computer interaction, and electrical engineering.
We are looking for motivated and detail-oriented undergraduate student standing with a strong GPA. This position may also be appropriate for a new graduate or master’s student looking to gain research experience. Students must commit an average 10 hours per week for three semesters, or 15 hours per week for two semesters in order to be considered.
Students will contribute to the project according to their skills and interests. There are two ‘teams’ of research assistants in our lab, the Data Collection team and the Analysis team.
Students interested in the Data Collection team will gain valuable research experience including running studies with infants and their caregivers, collecting and coding video and audio data, helping in study design or literature reviews, and attending a weekly lab meeting.
Students interested in the Analysis team, computer science, engineering or iSchool students, can gain experience developing new algorithms for automated activity detection or writing user-friendly apps for data collection. Electrical engineering students can also gain experience working on new hardware and firmware developments relevant to the project.
Opportunities to work on analyses and contribute to peer-reviewed publications are available for interested students.
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