Poster Session
Issue Date: August 01, 2019
Published Online: November 15, 2019
Updated: November 26, 2019
Using Machine Learning in an Automated Infant Motor Screening Tool for the Natural Environment
Author Affiliations & Notes
  • Select Rehabilitation, Glenview, IL, USA
  • Launch Bottle, Seattle, WA, USA
Article Information
Pediatric Evaluation and Intervention / Assessment/Measurement
Poster Session   |   August 01, 2019
Using Machine Learning in an Automated Infant Motor Screening Tool for the Natural Environment
American Journal of Occupational Therapy, August 2019, Vol. 73, 7311500036. https://doi.org/10.5014/ajot.2019.73S1-PO8001
American Journal of Occupational Therapy, August 2019, Vol. 73, 7311500036. https://doi.org/10.5014/ajot.2019.73S1-PO8001
Abstract

Date Presented 04/06/19

This poster describes a completed NIH Phase I study, a prototype of the Human Action Recognition Engine (HARE) utilizing the three-dimensional body mapping technology of the X-box Kinect to automate the extraction of infant postural and motor data of the infant during floor play with the parent in the natural environment of the home. This demonstrates the feasibility of an automated developmental risk screener to achieve state-of-the-art accuracy in the assessment of infant motor development.

Primary Author and Speaker: Teresa Fair-Field

Contributing Authors: Bharath Modayur