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- Installation
- Features: Digital biomarkers
- Features: Machine learning
- Extract digital biomarkers
- Train and save classifier
OpenHAC is built on existing software packages used to quantify behavioral characteristics and build machine learning classifiers. Our goal is to decrease the barrier of entry in digital phenotyping to researchers, scholars, and citizen scientists trying to understand the relationship between clinical disorders and their behavioral manifestations.
Through OpenHAC, a user can objectively and sensitively measure behavioral characteristics such as facial activity, oculomotion, patterns of movement, body key points, and heart rate. From those behavioral characteristics, they can measure clinically meaningful symptomatology such as emotional expressivity, pain expressivity, and more.
OpenHAC also makes it possible to create, analyze, and extract new digital biomarker features with it’s machine learning classification tools. Combining behavioral characteristics with manual classifications, a user can create effective classifiers for behavioral manifestations such as pain, drowsiness, activity level, and atypical movement––among many others.
OpenHAC Cole Hagen @ [email protected]