Open Human Activity Classification
A graphical user interface for researchers, scholars, and citizen scientists to peform digital biomarker extraction, video analysis, and machine learning creation and deployment.
About
OpenHAC is built on existing software packages used to quantify behavioral characteristics and build machine learning classifiers.
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 digital biomarkers 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.
Questions/getting started: Visit the wiki.
Aims
We hope this platform increases accessibility of objective and sensitive digital biomarker tools while also encouraging the exploration and creation of novel solutions for human activity phenotyping.
References
This repository was built upon the great works of many researchers and developers. Most notably:
View the Medium article.
Please reach out to hagencolej@gmail.com with any OpenHAC inquiries.