In recent years small, inexpensive inertial measurement units (IMUs) have been used clinically to monitor human body joint angles in order to assess the progression of, or recovery from, various diseases affecting movement, including Cerebral Palsy, Parkinsonโs Disease, and stroke. A representation of kinematic movement of a joint can be calculated by changes in the orientations of a pair IMUs placed on a limb above and below the joint. However, errors in the kinematic representation can occur if the IMUs are not correctly aligned with the kinematic model or if the initial position of the joint is not precisely known. In addition, due to the sensitivity of the IMUs to magnetic field distortions caused by nearby metal structures and electrical cables, the sensor coordinate frames can drift over time.
This thesis describes a new approach, the Anatomical Constraint Method (ACM), for reducing the effect of alignment and calibration errors using an algorithm to find a three degree of freedom correction for each IMU that minimizes the average extent to which the calculated joint angles exceed the expected anatomical range of motion. In addition, the algorithm reduces the effect of IMU drift by computing a correction for overlapping time intervals and using spherical linear interpolation to estimate continuous, time-dependent corrections. When the algorithm is applied to data from a network of IMUs designed to track the crawling movement of infants, results show a substantial improvement in the correlation of the kinematic representation of movement with recorded video images
