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

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 kneejoint 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.

Inertial Sensors for Human Motion Analysis: A Comprehensive Review

“…Classical KFs are normally used for the sensor fusion of gyroscope and accelerometer data to estimate the segment orientation [33], [40], [43], [50], [52], [57], [97], [139], the joint orientation [44], [82], [85], [95], [98], the segment location [74], and all of them, the segment and joint orientation and location [42]. KFs have also been used in the fusion of gyroscope, accelerometer, and magnetometer data to estimate segment orientation and location [136], [152].…”

Simultaneous exercise recognition and evaluation in prescribed routines: Approach to virtual coaches

“…Classical KFs are normally used for the sensor fusion of gyroscope and accelerometer data to estimate the segment orientation [33], [40], [43], [50], [52], [57], [97], [139], the joint orientation [44], [82], [85], [95], [98], the segment location [74], and all of them, the segment and joint orientation and location [42]. KFs have also been used in the fusion of gyroscope, accelerometer and magnetometer data to estimate segments orientation and location [136], [152].…”

Recreating the Motion Trajectory of a System of Articulated Rigid Bodies on the Basis of Incomplete Measurement Information and Unsupervised Learning

“…There are two sub-problems in the problem of human movement acquisition: the first concerns the acquisition of human movement in a controlled laboratory environment using, for example, MoCap technology or pressure platforms [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], the second in uncontrolled conditions of everyday life, e.g., with the use of IMU sensors and shoes pressure inserts [ 25 , 34 , 35 , 36 , 37 ]. The selection of the test environment does not affect the defined model of the rigid body system, while the measurement technique and the time of the measurements are changed.…”