Gait acquisition and analysis system for osteoarthritis based on hybrid prediction model. (October 2020)

• Record Type: Journal Article
• Authors: Chen, Fang; Cui, Xiwen; Zhao, Zhe; Zhang, Daoqiang; Ma, Cong; Zhang, Xinran; Liao, Hongen
• Is Part Of: Computerized medical imaging and graphics. Volume 85(2020)
• Publication Date: 2020-10
• Subjects: Gait analysis -- RGB-D camera -- Hybrid model -- Osteoarthritis; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2020.101782
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: The gait acquisition platform is built for osteoarthritis by using an RGB-D camera and the developed software of gait data recording Experimental results demonstrate that the gait analysis system can quantitatively predict gait anomalies with a high accuracy of 98.77% The gait acquisition and analysis system achieves quantitative recording and objective assessment of gait anomalies Abstract: Osteoarthritis (OA) is the most common type of joint-related diseases, which affects millions of people worldwide. Expensive and time-consuming medical imaging can provide precise structural description of knee joints, but lacks the functional descriptions. Gait analysis can provide functional descriptions of knee joints. However, orthopedic surgeons always observe the patient's gait qualitatively and perform subjective assessments through rating scales at present due to the lack of a quantitative gait analysis system. To solve these problems, a gait acquisition and analysis system is developed to provide a cheap, easy-to-use solution for quantitative recording and functional description of OA patients. Firstly, an automatic gait acquisition platform is designed for the clinical setting based on the RGB-D camera and the developed software of gait data recording. In addition, the effective working space of gait acquisition platform is evaluated for clinical applications by comparing with the ground-truth from infrared optical trackers. Secondly, the acquired gait data is analyzed with a novel hybrid prediction model to assess the gait anomalies quantitatively and objectively. In the hybrid model, the extracted features of gait data contain the manually-extracted features and the automatically-extracted features from Long Short-Term Memory network. Experimental results on real patients demonstrate that the proposed gait analysis system can quantitatively predict gait anomalies with a high accuracy of 98.77 %. Therefore, this gait acquisition and analysis system achieves quantitative recording and objective assessment of gait anomalies for clinical OA treatments.