Engineering Commons^™

Automated Intelligent Cueing Device To Improve Ambient Gait Behaviors For Patients With Parkinson's Disease, Nader Naghavi

Doctoral Dissertations

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods …

Comparison Of Machine Learning Models: Gesture Recognition Using A Multimodal Wrist Orthosis For Tetraplegics, Charlie Martin

The Journal of Purdue Undergraduate Research

Many tetraplegics must wear wrist braces to support paralyzed wrists and hands. However, current wrist orthoses have limited functionality to assist a person’s ability to perform typical activities of daily living other than a small pocket to hold utensils. To enhance the functionality of wrist orthoses, gesture recognition technology can be applied to control mechatronic tools attached to a novel fabricated wrist brace. Gesture recognition is a growing technology for providing touchless human-computer interaction that can be particularly useful for tetraplegics with limited upper-extremity mobility. In this study, three gesture recognition models were compared—two dynamic time-warping models and a hidden …

Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han

Public Health Faculty Publications

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …

Automatic Detection Of Dynamic And Static Activities Of The Older Adults Using A Wearable Sensor And Support Vector Machines, Jian Zhang, Rahul Soangra, Thurmon E. Lockhart

Physical Therapy Faculty Articles and Research

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant …

A Convolutional Neural Network For Fast Fluence Estimation In Complex Tissues, Nicholas Blasey, Geoffrey P. Luke

ENGS 88 Honors Thesis (AB Students)

Photoacoustic (PA) imaging is a non-invasive diagnostic imaging technique that gives images of photoabsorbers based on their absorption of optical energy. These optical absorption properties can then be linked to important tissue properties. For the method to be quantitative, however, it is necessary to have an accurate estimation of the light fluence in the tissue. The current gold standard in addressing the fluence estimation problem, a Monte Carlo Simulation, is costly in time and computation. In this work, we developed a deep neural network to quickly and accurately estimate light fluence in arbitrary tissue types and geometries. The network was …

Neurobiological Markers For Remission And Persistence Of Childhood Attention-Deficit/Hyperactivity Disorder, Yuyang Luo

Dissertations

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in children. Symptoms of childhood ADHD persist into adulthood in around 65% of patients, which elevates the risk for a number of adverse outcomes, resulting in substantial individual and societal burden. A neurodevelopmental double dissociation model is proposed based on existing studies in which the early onset of childhood ADHD is suggested to associate with dysfunctional subcortical structures that remain static throughout the lifetime; while diminution of symptoms over development could link to optimal development of prefrontal cortex. Current existing studies only assess basic measures including regional brain activation …

Ultrasound Guided Diffuse Optical Tomography For Breast Cancer Diagnosis: Algorithm Development, K M Shihab Uddin

McKelvey School of Engineering Theses & Dissertations

According to National Breast Cancer Society, one in every eight women in United States is diagnosed with breast cancer in her lifetime. American Cancer Society recommends a semi-annual breast-cancer screening for every woman which can be heavily facilitated by the availability of low-cost, non-invasive diagnostic method with good sensitivity and penetration depth. Ultrasound (US) guided Diffuse Optical Tomography (US-guided DOT) has been explored as a breast-cancer diagnostic and screening tool over the past two decades. It has demonstrated a great potential for breast-cancer diagnosis, treatment monitoring and chemotherapy-response prediction. In this imaging method, optical measurements of four different wavelengths are …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations & Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Machine Learning Towards General Medical Image Segmentation, Clara Tam

Electronic Thesis and Dissertation Repository

The quality of patient care associated with diagnostic radiology is proportionate to a physician's workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object's contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, …