Upper Body Joint Angle Calculation and Analysis Using Multiple Inertial Measurement Units

Abstract

Abstract Understanding how workers move in professions that require unnatural posture can help find solutions to work-related musculoskeletal disorders (WMSDs) that are common in such occupations. IMU (inertial measurement unit)-based measurement is a potential alternative to motion capture systems that are widely used for body posture measurement, but lack portability. This paper applies three sensor fusion algorithms, Madgwick’s filter, Kalman filter, and complementary filter, to estimate joint angles of the shoulder and arm based on the data collected from multiple IMUs. In the implementation of the algorithms, quaternions are used to represent the ideal orientation and avoid singularities. For experiments, three IMUs were placed on the lower arm, upper arm, and upper torso of a subject for two representative motions: reaching up and reaching across a desk. Data from the three IMUs were taken simultaneously and post-processed to estimate the elbow and shoulder joint angles. These estimations were compared with the reference data collected using a motion capture system to determine the overall accuracy for each sensor fusion method. Parameters for each algorithm were optimized to minimize the overall joint angle errors.