Development of a novel motion capture and gait analysis system for rat locomotion. (18th August 2021)

• Record Type: Journal Article
• Authors: Dai, Chuankai; Lyu, Xiaodong; Meng, Fei; He, Jiping; Huang, Qiang; Fukuda, Toshio
• Is Part Of: Advanced robotics. Volume 35:Number 16(2021)
• Page Start: 961
• Page End: 980
• Publication Date: 2021-08-18
• Subjects: Rodents kinematics -- motion capture -- gait analysis -- image processing -- artificial neural networks; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2021.1957013
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : This paper proposes a marker-based motion capture system for rat motion detection and gait analysis. Motion capture in small animals such as rodents is more challenging than in humans because of their small bodies and rapid motion. Existing algorithms have poor applicability in rat motion capture in environments outside the studio. Moreover, gait analysis targeting on the rat is not performed by existing motion capture software. Our method consists of four procedures. First, Region of Interest (ROI) is extracted from the background using the inter-frame difference method and a depth filter. Second, a double-threshold marker detection method is used to detect markers in ROI and a marker shape filter is used to classify the markers. Third, a marker corrector is designed to modify missing and incorrect markers. Finally, a deep learning network is used to analyse the gait trajectory to classify rat as healthy, injured, or rehabilitated. The experimental result shows that marker recognition accuracy is 99.33%, higher than that of most existing software. The validation accuracy of the network is 100% and the loss is 0.0001. This method is conductive to the development of motion capture systems for small animals and research into the gait kinematics of rodents. GRAPHICAL ABSTRACT: UF0001