M.S. (Master of Science)
Department of Mechanical Engineering
A prominent area in biomechanics revolves around finding solutions to common issues like work-related musculoskeletal disorders (WMSDs). These are common in professions associated with unnatural postures like commercial fishing and farming. Understanding how these professions move on a day-to-day basis can help find solutions to WMSDs. While a motion capture system might be the most common equipment for body posture measurement, it lacks the portability to work in remote environments like commercial fishing and farming. Therefore, joint angle estimation using IMUs (Inertial Measurement Units) could provide a potential alternative.
An IMU consists of a triaxial accelerometer, gyroscope, and magnetometer, which can each be used to build an estimation of the sensor orientation with respect to the world/fixed frame of reference. To estimate orientations, the gyroscope data of angular velocity can be numerically integrated, or the accelerometer together with the magnetometer can be used. However, these sensors are susceptible to gyroscope drift due to the integration process, accelerometer noise during dynamic conditions, and magnetic hard and soft iron distortions, respectively.
In this thesis, three sensor fusion algorithms that have been developed to account for the issues while combining the benefits associated with each sensor were applied to estimate joint angles of the shoulder and arm effectively. Madgwick's filter utilizes the gradient descent algorithm to quickly narrow in on an orientation estimation. Kalman filters are an iterative two-step process involving a prediction followed by an update, using a knowledge of measurement uncertainties and their Gaussian distributions in each iteration. Complementary filters use a weighted average to combine the sensor data one at a time and find an angle and axis of rotation to tilt the orientation closer to the actual value.
To demonstrate the application of the three algorithms for arm motion tracking, three IMUs were placed on each part of an arm (lower, upper, and shoulder). The data was then post-processed to estimate the elbow and shoulder joint angles. The estimations were compared with the reference data collected using a motion capture system. Furthermore, parameters for each sensor fusion algorithm were determined to minimize error and compare algorithm accuracy.
Freedkin, Aaron S., "Upper Body Joint Angle Calculation and Analysis Using Multiple inertial Measurement Units" (2022). Graduate Research Theses & Dissertations. 7041.
Northern Illinois University
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