International Journal on Advanced Science, Engineering and Information Technology, Vol. 10 (2020) No. 2, pages: 430-437, DOI:10.18517/ijaseit.10.2.10814

Knee Joint Angle Measuring Portable Embedded System based on Inertial Measurement Units for Gait Analysis

Dagoberto Mayorca-Torres, Julio C. Caicedo-Eraso, Diego H. Peluffo-Ordóñez

Abstract

Inside clinical research, gait analysis is a fundamental part of the functional evaluation of the human body's movement. Its evaluation has been carried out through different methods and tools, which allow early diagnosis of diseases, and monitoring and assessing the effectiveness of therapeutic plans applied to patients for rehabilitation. The observational method is one of the most used in specialized centers in Colombia; however, to avoid any possible errors associated with the subjectivity observation, technological tools that provide quantitative data can support this method. This paper deals with the methodological process for developing a computational tool and hardware device for the analysis of gait, specifically on articular kinematics of the knee.  This work develops a prototype based on the fusion of inertial measurement units (IMU) data as an alternative for the attenuation of errors associated with each of these technologies. A videogrammetry technique measured the same human gait patterns to validate the proposed system, in terms of accuracy and repeatability of the recorded data. Results showed that the developed prototype successfully captured the knee-joint angles of the flexion-extension motions with high consistency and accuracy in with the measurements obtained from the videogrammetry technique. Statistical analysis (ICC and RMSE) exhibited a high correlation between the two systems for the measures of the joint angles. These results suggest the possibility of using an IMU-based prototype in realistic scenarios for accurately tracking a patient’s knee-joint kinematics during a human gait.

Keywords:

IMU; gait analysis; motion analysis; knee-joint angle; kalman filter.

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