Physically Consistent Whole-Body Kinematics Assessment Based on an RGB-D Sensor. Application to Simple Rehabilitation Exercises

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 10; p. 2848
• Main Authors: Colombel, Jessica, Bonnet, Vincent, Daney, David, Dumas, Raphael, Seilles, Antoine, Charpillet, François
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 17.05.2020; MDPI AG
• Subjects: Algorithms; Biomechanical Phenomena; Biomechanics; Engineering Sciences; Exercise Therapy; extended Kalman filter and rehabilitation; Healthy Volunteers; Humans; markerless human motion analysis; Mechanics; Photogrammetry; Rehabilitation; markerless human motion analysis; extended Kalman filter and rehabilitation; Markerless human motion analysis; Extended Kalman filter and rehabilitation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20102848

This work proposes to improve the accuracy of joint angle estimates obtained from an RGB-D sensor. It is based on a constrained extended Kalman Filter that tracks inputted measured joint centers. Since the proposed approach uses a biomechanical model, it allows physically consistent constrained joint angles and constant segment lengths to be obtained. A practical method that is not sensor-specific for the optimal tuning of the extended Kalman filter covariance matrices is provided. It uses reference data obtained from a stereophotogrammetric system but it has to be tuned only once since it is task-specific only. The improvement of the optimal tuning over classical methods in setting the covariance matrices is shown with a statistical parametric mapping analysis. The proposed approach was tested with six healthy subjects who performed four rehabilitation tasks. The accuracy of joint angle estimates was assessed with a reference stereophotogrammetric system. Even if some joint angles, such as the internal/external rotations, were not well estimated, the proposed optimized algorithm reached a satisfactory average root mean square difference of 9.7 ∘ and a correlation coefficient of 0.8 for all joints. Our results show that an affordable RGB-D sensor can be used for simple in-home rehabilitation when using a constrained biomechanical model.