Search

Abstract: Falls are one of the most frequent causes of injury in the elderly and ambulatory individuals with neuromuscular impairments. Standing balance impairment is among the most consistent predictors of future falls. Furthermore, many individuals with neuromusculoskeletal conditions use a wheelchair for daily ambulation and often exhibit degraded trunk control during dynamic tasks, requiring assistance in seated stability. Therefore, implementing outcome measures that identify static balance difficulties may lead to more effective rehabilitation, and reduced future fall risk and fall severity in affected individuals. Characterizing the dynamic balance and neuromuscular control mechanisms are essential for identifying underlying impairments, implementing targeted rehabilitation, and developing assistive technologies. The overall goal of this thesis is to contribute toward developing methodologies for instrumented static and dynamic balance assessment with high sensitivity and responsiveness, allowing for a better understanding of the mechanisms of postural control. This thesis aimed to (1) develop and validate algorithms for reliable assessment of static balance using wearable technology, with the capability of being integrated into clinical tests for individuals with neuromuscular impairments; and (2) characterize the relationship between dynamic balance and risk of loss of balance and identify the roles of neuromuscular mechanisms involved in seated stability. First, we validated an algorithm for characterizing static balance using wearable technology against measurements of gold-standard in-lab equipment. We showed that our proposed method could provide accurate kinematics and kinetics measures and could be recommended for monitoring standing balance. Second, we used the validated algorithm to perform a static balance evaluation using wearable technology for ambulatory individuals with incomplete spinal cord injury (iSCI) with mild balance deficits during standing under various conditions. Our method enabled characterizing standing balance in this group compared to able-bodied participants with sufficient resolution and discriminatory ability for objective balance evaluation. Third, we used the validated algorithm to compare the postural control strategy between the same iSCI and able-bodied participants by characterizing their trunk-leg movement coordination under different sensory conditions. We observed trunk-leg movement coordination showed high sensitivity, discriminatory ability, and excellent test-retest reliability to identify changes in postural control strategy post-iSCI. Fourth, we investigated, in a clinical setting, the use of the validated algorithm above and the integration of wearable technology into a clinical scale test for objective outcome evaluation of balance rehabilitation in elderly with moderate-to-severe balance impairments. Our method enabled identifying and characterizing underlying causes of impaired balance pre- and post-rehabilitation with high sensitivity to subtle changes in balance. Fifth, we determined the limit of dynamic seated stability as a function of the trunk kinematics relative to the base of support. We experimentally validated the predicted limit of stability using traditional motion capture cameras. We then validated an algorithm to use wearable technology for assessing dynamic seated stability and risk of loss of balance against a gold-standard system. Sixth, we characterized the neuromuscular mechanisms involved in human sitting by identifying a nonlinear physiologically-meaningful neuromechanical model of seated stability. The model predicted the trunk sway behaviour during perturbed sitting with high accuracy. Our method accounted for physiological uncertainties while allowing for real-time tracking and correction of parameters’ variations due to external disturbances and muscle fatigue. Seventh, we identified the high-level task goals of the neural control for regulating dynamic seated stability using nonlinear control theory. We observed the neural control might use trunk angular kinematics, primarily angular acceleration, as the input to achieve near-minimum muscle activation while keeping the deviations of the trunk angular position and acceleration sufficiently small. The practical outcome of this research toward static balance assessment is the development of algorithms used with wearable sensors for clinical objective balance assessment and characterization of complex balance mechanisms during static quiet stance. Such algorithms may provide a significant increase in the sensitivity of diagnosis of impaired balance for ambulatory individuals with iSCI with mild balance deficits and elderly with moderate-to-severe balance impairments. The practical outcomes of this research toward dynamic balance assessment are: (a) obtaining dynamic limits of stability for sitting; (b) the development of an algorithm for assessing the risk of loss of balance using wearable technology; (c) the development of a novel methodologies for a mechanistic understanding of the several neuromuscular stabilization mechanisms and high-level task goals of the neural control for maintaining dynamic stability.

This dissertation describes the development of a non-invasive framework for better understanding the motion of bones in knee joints in 3D space using ultrasound and low complexity image registration for applications in the domain of biomedical research and treatment planning related to knee joints. Kinematic analysis allows the motion of individual bones in the knee joint to be measured. These motion measurements provide significant insights into normal and abnormal joint trajectories which can lead to improved artificial joint component design, superior diagnosis for ligament injuries and adequate therapeutic strategies to be formulated. Knee joint kinematics is complex in a sense that the natural motions of flexion, extension and internal, external rotation are coupled. During flexion, the two main bones in a knee joint, the tibia and femur, do not bend like a door hinge, instead they rotate about several specific axes. This type of motion makes it crucial that the joint kinematics be evaluated in 3D space. The existing techniques, whether they use x-ray, optoelectronic, ultrasonic or video motion capture to track the bone movements under the skin, are somewhat prone to invasiveness or ionizing radiation or skin wobbling during dynamic activities. In this thesis, a 2D B-mode ultrasound based multi-slice H shaped array sensor and its calibration technique are proposed. This novel calibrated array is capable of tracking the relative motion of the bones in a knee joint in 3D space without the requirement of any invasive implantation of beads in the knee but it still provides the required accuracy when compared to the current clinical standard Roentgen Stereo Analysis (RSA). Finally, a single element ultrasound sensor based method using image registration for rigid body motion parameter estimation is proposed. This approach not only facilitates joint kinematic determination but also guides pre- and post-surgery planning of the knee joint based non-invasive A-mode ultrasound. The unique feature of the proposed methods are the estimation of the six rigid-body transform parameters with large convergence radius with enough precision, robustness to ultrasound (US) speckle noise and speed of convergence.

Abstract: Conventional cytometry uses fluorescently labelled cells that are laborious and costly to prepare. Using a label-free technique, individual cells can be differentiated by light scattering patterns, a result of different physical attributes. This thesis describes the development of a microfluidic label-free cytometer. The key components include a microfluidic chip, a laser light scattering system for cell characterization, and a microfluidic flow control system. A PDMS microfluidic chip was developed and described in detail. A customised flow control system with sorting functionality was constructed to respond faster than commercial pumps. The linear relationship between applied pump pressure and average flow speed was confirmed experimentally, as well as a sorting flow response of 48.3ms or less. Results from laser light scatter experiments with different biological cells were presented, where scatter patterns for each cell type were visually distinguishable.