Targeted Movement Pattern Recognition for Infants with Perinatal Brachial Plexus Injury

This thesis presents work toward a novel rehabilitation tool for infants with limited arm movement such as those who sustain a perinatal brachial plexus injury (PBPI). PBPI is a traction injury to peripheral nerves that occurs during the birth process. An injury to the upper trunk of the plexus (C5-6 spinal nerves) partially or fully denervates the skin of the upper arm and muscles of the elbow (i.e., biceps, brachialis) and shoulder (i.e., Deltoid, Sternal Pectoralis Major, Rotator Cuff). Initially, PBPI is typically treated with Passive Range of Motion (PROM) and positioning led by a physical therapist or occupational therapist. If recovery is limited, nerve microsurgery is indicated by 6 months of age. Recovery in infants with PBPI varies from 38 to 80% of infants depending on the initial condition and severity, as well as the rate of reinnervation. Yet, through technology, there may be methods available to increase the rate of recovery, leading to greater use of the affected arm. Infants as young as 3 months of age have been found to increase arm and leg movements through a paradigm of contingent reinforcement (i.e. rewarding desired movement patterns with audiovisual stimulation such as an overhead mobile). Before a device can be fully constructed to provide contingent reinforcement for desired arm movements, those movements must be consistently detectable. Thus, for this thesis project, I studied automatic detection of the arm movements desired for young infants with PBPI using arm acceleration data.Acceleration data was acquired from a wrist-worn sensor as an adult volunteer moved her arm in the desired motion. I implemented a template-matching algorithm based on taking the dot-product of a moving window of 3D acceleration vectors with template acceleration patterns, where the templates were movement samples deemed targeted and rehabilitative by an experienced physical therapist. I found that the algorithm detected rehabilitative movements with an accuracy of ~90%. The algorithm never identified a movement that was deemed as undesirable for this population of infants, by the physical therapist. These results reveal the potential for the template matching algorithm to be used in a contingent reinforcement paradigm capable of activating a toy to encourage infants with PBPI to make targeted rehabilitative arm movements

Correction of Kinematic Data from a Self-Initiated Prone Position Crawler Trainer for Infants with Cerebral Palsy

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, Parkinson’s 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

EEG OSCILLATORY ACTIVITIES FROM HUMAN MOTOR BRAIN

Motor skills are essential in people’s daily life in exploring and interacting with the ambient environment. Impairments to motor functions affect the acquisition of motor skills, which not only reduce the quality of life, but also impose heavy economic burdens to sufferers and their families. Oscillatory activities in electroencephalography (EEG), such as the mu rhythm, present functional correlation to motor functions, which provide accessible windows to understand underlying neural mechanism in healthy persons and perform diagnoses in patients with various motor impairments. It is thus of significant importance to further investigate classic and/or identify new motor-related EEG oscillatory activities. In this dissertation, EEG oscillations from both infants and adults are investigated to uncover motor-related neural information noninvasively from the human brain regarding their developmental changes and movement representations of body parts, respectively. In typical developing infants at 5-7 months of age, knowledge about mu rhythm development is expanded by capturing subtle developmental changes of its characteristics in a fine age resolution, through the development of new spatio-spectral analysis of EEG data recorded longitudinally on a weekly basis. In adults, motor tasks involving fine body parts are studied to investigate EEG resolutions in decoding movements/motor imageries of individual fingers, which have only been addressed in large body parts in literature. Discriminative information in EEG oscillations about motor tasks of fine body parts is revealed through the discovery of a novel type of spectral structures in EEG, which exhibits better sensitivity to movements of fine body parts than the classic mu rhythm. The findings in this dissertation broaden the scope of neural information in EEG oscillations in relation to motor functions, and contribute to the understanding about human motor functions at various life stages. These results and technologies are promising to be translated to patient studies in the future