Inclination measurement of human movement using a 3-D accelerometer with autocalibration

Abstract

In the medical field, accelerometers are often used for measuring inclination of body segments and activity of daily living (ADL) because they are small and require little power. A drawback of using accelerometers is the poor quality of inclination estimate for movements with large accelerations. This paper describes the design and performance of a Kalman filter to estimate inclination from the signals of a triaxial accelerometer. This design is based on assumptions concerning the frequency content of the acceleration of the movement that is measured, the knowledge that the magnitude of the gravity is 1 g and taking into account a fluctuating sensor offset. It is shown that for measuring trunk and pelvis inclination during the functional three-dimensional activity of stacking crates, the inclination error that is made is approximately 20 root-mean square. This is nearly twice as accurate as compared to current methods based on low-pass filtering of accelerometer signals.