This project focuses on using inertial measurement units (IMUs) in the "wild," meaning using IMUs to capture state-specific data in a non-laboratory and non-controlled environment. IMUs are generally used to capture linear acceleration and angular velocity data using gyroscopes and accelerometers. Through some integration, these linear accelerations and angular velocities can then be used to determine the position of the IMU,which provides a useful way to capture biomechanical data such as joint angles and body positions. However, one of the largest issues with capturing real-time position data is the noise apparent not only in the environment, but also in the sensors themselves. Using the integration method to determine positions and joint angles introduces drift error - a systemic artifact due to the integration that causes the data to shift towards one direction. Existing studies have shown that applying a Kalman filter could ameliorate the effects of this drift (Reference).
Our project aims to apply the Kalman filter to the raw IMU data in order to find an accurate measurement of the orientation of the head/neck.
How can we integrate biomechanical models with the Kalman filters to more accurately sense orientations from IMUs?
Can multiple IMU's be used to measure neck angle?
collected experimental, and found the ground truth - video tracking and now have raw data from xsense (linear accel and angular velocity) compared to xsense processed data. Motivation of leg as a simpler system, and assume head neck a pin joint
plotted the data for a sanity check and . integrated of angular velocity of the sensors of right leg and shank, clearly there's drift over time
Home: BIOE-ME 485 Spring 2017
In order to show the drift error inherent in the IMU raw data, we needed to take the raw data from the IMUs and integrate to get position data.