Abstract—This work uses Kalman and complementary filters to integrate data from inertial sensors (accelerometer and gyroscope) for the purpose of tracking human knee joint movement and then compares results from these filters. A combination of a tri-axial accelerometer and a tri-axial gyroscope (MPU-9150) was used as sensor. mbed NXP LPC1768 microcontroller and XBee wireless communication modules were also used. Both filters show high repeatability (<1.0°), indicating usability. Though the Kalman filter is the most appropriate (theoretically) for data fusion of noisy measurements, it is computationally intensive. Results in this work however show that the complementary filter (which is cheaper and much simpler to implement) works equally well for this application. 
 

Index Terms—accelerometer, gyroscope, knee joint, complementary filter, Kalman filter, range-of-motion

Cite: Olubiyi O. Akintade and Lawrence O. Kehinde, "Comparison of Data Fusion Techniques for Human Knee Joint Range-of-Motion Measurement Using Inertial Sensors," International Journal of Electronics and Electrical Engineering, Vol. 5, No. 2, pp. 127-134, April 2017. doi: 10.18178/ijeee.5.2.127-134