3D motion capture system for assessing patient motion during Fugl‐Meyer stroke rehabilitation testing

Abstract

The authors introduce a novel marker‐less multi‐camera setup that allows easy synchronisation between 3D cameras as well as a novel pose estimation method that is calculated on the fly based on the human body being tracked, and thus requires no calibration session nor special calibration equipment. They show high accuracy in both calibration and data merging and is on par with equipment‐based calibration. They deduce several insights and practical guidelines for the camera setup and for the preferred data merging methods. Finally, they present a test case that computerises the Fugl‐Meyer stroke rehabilitation protocol using the authors’ multi‐sensor capture system. They conducted a Helsinki‐approved research in a hospital in which they collected data on stroke patients and healthy subjects using their multi‐camera system. Spatio‐temporal features were extracted from the acquired data and machine learning‐based evaluations were applied. Results showed that patients and healthy subjects can be correctly classified at a rate of above 90%. Furthermore, they show that the most significant features in the classification are strongly correlated with the Fugl‐Meyer guidelines. This demonstrates the feasibility of a low‐cost, flexible and non‐invasive motion capture system that can potentially be operated in a home setting.

Keywords

• stroke patients
• machine learning-based evaluations
• Fugl-Meyer guidelines
• noninvasive motion capture system
• 3D motion capture system
• patient motion