Skeletal TRacking Enhanced with Anatomically Correct Kinematics for Exergames and Rehabilitation (STREACKER)

Type: National Project

Duration: from 2018 Oct 01 to 2019 Sep 30

Financed by: FCT

Prime Contractor: INESC-ID (Other)

This project envisions to accurately track body segment orientations, for a given arbitrary posture, using machine learning to select the optimal vector orthogonalization technique that best correlates with the posture. By the end of the project, we expect to develop a markerless, cost effective, and reliable motion capture platform composed by an array of Kinect sensors that runs our algorithm in order to build plausible skeletons with anatomically correct body segments. The potential economic value of the technology resides mostly in the video game and fitness industries as well as rehabilitation and motion capture laboratory niches. The major goal of this project proposal is to develop a new algorithm for skeletal tracking with anatomically correct segment orientation based the combination of vector orthogonalization and advanced machine learning techniques. We aim to augment current markerless motion capture used in exergames and rehabilitation.

Partnerships

  • Hospital Professor Doutor Fernando Fonseca, EPE (HFF, EPE (Other) -Sintra
  • Instituto de Engenharia Mecânica (Other) - Lisbon, Portugal
  • University of Texas at Austin, Mechanical Engineering Department (University) -USA

Principal Investigators

Members