Two papers accepted to Ubicomp/ISWC 2020!read more
Best Student Paper Award at Learning@Scale 2020
Congratulations to lead author Qiaosi (Chelsea) Wang and CBA co-authors Hong Li and Thomas Ploetz!
Daniel Scarafoni wins Outstanding Achievement in Robotic Orchestration at the UnixWorld Challenge!read more
Hyeok wins the consolation prize in the Emteq activity recognition challenge!read more
6 papers accepted at Ubicomp/ISWC 2019!read more
CBA moves to CODA!
Find us on the 15th floor! Plus, we have spectacular views!
ACII Keynote: "Quo Vadis, Computational Behavior Analysis?"read more
CBA Lab @GeorgiaTech
We are the Computational Behavior Analysis (CBA) research lab at Georgia Institute of Technology.
Our research agenda focuses on applied machine learning, that is developing systems and innovative sensor data analysis methods for real world applications. Primary application domain for our work is computational behavior analysis where we develop methods for automated and objective behavior assessments in naturalistic environments. Main driving functions for our work are “in the wild" deployments and as such the development of systems and methods that have a real impact on peoples’ lives.
A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study
Masked reconstruction based self-supervision for human activity recognition
Estimation of Instantaneous Oxygen Uptake during Exercise and Daily Activities using a Wearable Cardio-Electromechanical and Environmental Sensor
IMUTube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition
Deep learning-based automated speech detection as a marker of social functioning in late-life depression
Automated General Movement Assessment for Perinatal Stroke Screening in Infants
Recognition Of Atypical Behavior In Autism Diagnosis From Video Using Pose Estimation Over Time
Sensing Affect to Empower Students: Learner Perspectives on Affect-Sensitive Technology in Large Educational Contexts
Our research is supported by grants from: