Biomedical Engineering and Bioengineering Commons^™

Multi-Classifier Fusion Strategy For Activity And Intent Recognition Of Torso Movements, Abhijit Kadrolkar

Doctoral Dissertations

As assistive, wearable robotic devices are being developed to physically assist their users, it has become crucial to develop safe, reliable methods to coordinate the device with the intentions and motions of the wearer. This dissertation investigates the recognition of user intent during flexion and extension of the human torso in the sagittal plane to be used for control of an assistive exoskeleton for the human torso. A multi-sensor intent recognition approach is developed that combines information from surface electromyogram (sEMG) signals from the user’s muscles and inertial sensors mounted on the user’s body. Intent recognition is implemented by following …

Development Of A Portable Cmos Time-Domain Fluorescence Lifetime Imager, Hongtao Wang

Doctoral Dissertations

Modern laboratory equipments to measure the excited-state lifetime of fluorophores usually include an expensive picosecond pulsed-laser excitation source, a fragile photomultiplier tube, and a large instrument body for optics. A portable and robust device to make fluorescence lifetime measurement in nanosecond scale is of great attraction for chemists and biologists. This dissertation reports the development of a portable LED time-domain fluorimeter from an all-solid-state discrete-component prototype to its advanced CMOS integrated circuit implementation. The motivation of the research is to develop a multiplexed fluorimeter for point-of-care diagnosis. Instruments developed by this novel method have higher fill factor, are more portable, …

Adaptive Kernel Estimation For Enhanced Filtering And Pattern Classification Of Magnetic Resonance Imaging: Novel Techniques For Evaluating The Biomechanics And Pathologic Conditions Of The Lumbar Spine, Nicholas Vincent Battaglia

Doctoral Dissertations

This dissertation investigates the contribution the lumbar spine musculature has on etiological and pathogenic characteristics of low back pain and lumbar spondylosis. This endeavor necessarily required a two-step process: 1) design of an accurate post-processing method for extracting relevant information via magnetic resonance images and 2) determine pathological trends by elucidating high-dimensional datasets through multivariate pattern classification. The lumbar musculature was initially evaluated by post-processing and segmentation of magnetic resonance (MR) images of the lumbar spine, which characteristically suffer from nonlinear corruption of the signal intensity. This so called intensity inhomogeneity degrades the efficacy of traditional intensity-based segmentation algorithms. Proposed …

Point Of Care Diagnostics And Health Monitoring Devices, Akshaya Shanmugam

Doctoral Dissertations

Existing disease screening methods mostly rely on symptom based diagnosis. This is mainly because of lack of accessibility and cost associated with the tests. Testing for the presence of the disease after the onset of symptoms has a negative impact on chances of survival and treatment costs. Miniaturized low cost diagnostic devices that can be used outside the hospital setting can provide continuous health monitoring and aid in early diagnosis. This thesis presents techniques to develop such disease screening and health monitoring devices. The techniques presented here focus on medical devices that can benefit from microfluidic devices, fluorescence imaging, and …

Detecting, Segmenting And Tracking Bio-Medical Objects, Mingzhong Li

Doctoral Dissertations

"Studying the behavior patterns of biomedical objects helps scientists understand the underlying mechanisms. With computer vision techniques, automated monitoring can be implemented for efficient and effective analysis in biomedical studies. Promising applications have been carried out in various research topics, including insect group monitoring, malignant cell detection and segmentation, human organ segmentation and nano-particle tracking.

In general, applications of computer vision techniques in monitoring biomedical objects include the following stages: detection, segmentation and tracking. Challenges in each stage will potentially lead to unsatisfactory results of automated monitoring. These challenges include different foreground-background contrast, fast motion blur, clutter, object overlap and …