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Team Members

  • Richard Hsiao
  • Michael Fanton


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.

Research Questions

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


  1. Collected experimental data of knee flexion using high speed motion capture camera (120 bpm, 80 bpm, 40 bpm) and XSENS IMU tracking system.
    By using high speed mo-cap, markers on the shank, knee joint, and thigh, and a goniometer, we were able to capture the ground truth of the biomechanical movement and state data during knee flexion.We used knee flexion as a starting point as it was easier to collect the data since the XSENS system has marker data for the thigh and shank. These steps were also a starting point for another group project (Using Inertial Measurement Units to Calculate Knee Flexion Angle) and steps for the knee flexion experiment can be found there. 

  2. Processed the motion-capture data in the knee flexion/extension to capture knee angle with markers

  3. Processed the raw IMU data of knee flexion to obtain position by integrating the linear acceleration and angular velocities
    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. This step was to illustrate the error. 




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.