3D motion capture system for assessing patient motion during Fugl‐Meyer stroke rehabilitation testing. Issue 7 (28th August 2018)

• Record Type: Journal Article
• Authors: Eichler, Nadav; Hel‐Or, Hagit; Shimshoni, Ilan; Itah, Dorit; Gross, Bella; Raz, Shmuel
• Is Part Of: IET computer vision. Volume 12:Issue 7(2018)
• Page Start: 963
• Page End: 975
• Publication Date: 2018-08-28
• Subjects: calibration -- learning (artificial intelligence) -- cameras -- pose estimation -- patient rehabilitation -- medical image processing -- biomedical optical imaging -- feature extraction; stroke patients -- machine learning-based evaluations -- Fugl-Meyer guidelines -- noninvasive motion capture system -- 3D motion capture system -- patient motion -- Fugl-Meyer stroke rehabilitation testing -- human body -- equipment-based calibration -- Fugl-Meyer stroke rehabilitation protocol -- Helsinki-approved research -- spatiotemporal feature extraction -- marker-less multicamera setup -- multi-sensor capture system -- pose estimation method -- data merging; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5274
• Languages: English
• ISSNs: 1751-9632

Abstract

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.