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- Genre: Masters Thesis
Description
Muscular weakness is a common manifestation for Stroke survivors and for patients with Anterior Cruciate Ligament reconstruction leading to reduced functional independence, especially mobility. Several rigid orthotic devices are being designed to assist mobility. However, limitations in majority of these devices are: 1) that they are constrained only to level walking applications, 2) are mostly bulky and rigid lacking user comfort. For these reasons, rehabilitation using soft-robotics can serve as a powerful modality in gait assistance and potentially accelerate functional recovery. The characteristics of soft robotic exosuit is that it’s more flexible, delivers high power to weight ratio, and conforms with the user’s body structure making it a suitable choice. This work explores the implementation of an existing soft robotic exosuit in assisting knee joint mechanism during stair ascent for patients with muscular weakness. The exosuit assists by compensating the lack of joint moment and minimizing the load on the affected limb. It consists of two I-cross-section soft pneumatic actuators encased within a sleeve along with insole sensor shoes and control electronics. The exosuit actuators were mechanically characterized at different angles, in accordance to knee flexion in stair gait, to enable the generation of the desired joint moments. A linear relation between the actuator stiffness and internal pressure as a function of the knee angle was obtained. Results from this characterization along with the insole sensor outputs were used to provide assistance to the knee joint. Analysis of stair gait with and without the exosuit ‘active’ was performed, using surface electromyography (sEMG) sensors, for two healthy participants at a slow walking speed. Preliminary user testing with the exosuit presented a promising 16% reduction in average muscular activity of Vastus Lateralis muscle and a 3.6% reduction on Gluteus Maximus muscle during the stance phase and unrestrained motion during the swing phase of ascent thereby demonstrating the applicability of the soft-inflatable exosuit in rehabilitation.
ContributorsMuthukrishnan, Niveditha (Author) / Polygerinos, Panagiotis (Thesis advisor) / Lockhart, Thurmon (Committee member) / Peterson, Daniel (Committee member) / Arizona State University (Publisher)
Created2018
Description
The past decade has seen a drastic increase in collaboration between Computer Science (CS) and Molecular Biology (MB). Current foci in CS such as deep learning require very large amounts of data, and MB research can often be rapidly advanced by analysis and models from CS. One of the places where CS could aid MB is during analysis of sequences to find binding sites, prediction of folding patterns of proteins. Maintenance and replication of stem-like cells is possible for long terms as well as differentiation of these cells into various tissue types. These behaviors are possible by controlling the expression of specific genes. These genes then cascade into a network effect by either promoting or repressing downstream gene expression. The expression level of all gene transcripts within a single cell can be analyzed using single cell RNA sequencing (scRNA-seq). A significant portion of noise in scRNA-seq data are results of extrinsic factors and could only be removed by customized scRNA-seq analysis pipeline. scRNA-seq experiments utilize next-gen sequencing to measure genome scale gene expression levels with single cell resolution.
Almost every step during analysis and quantification requires the use of an often empirically determined threshold, which makes quantification of noise less accurate. In addition, each research group often develops their own data analysis pipeline making it impossible to compare data from different groups. To remedy this problem a streamlined and standardized scRNA-seq data analysis and normalization protocol was designed and developed. After analyzing multiple experiments we identified the possible pipeline stages, and tools needed. Our pipeline is capable of handling data with adapters and barcodes, which was not the case with pipelines from some experiments. Our pipeline can be used to analyze single experiment scRNA-seq data and also to compare scRNA-seq data across experiments. Various processes like data gathering, file conversion, and data merging were automated in the pipeline. The main focus was to standardize and normalize single-cell RNA-seq data to minimize technical noise introduced by disparate platforms.
Almost every step during analysis and quantification requires the use of an often empirically determined threshold, which makes quantification of noise less accurate. In addition, each research group often develops their own data analysis pipeline making it impossible to compare data from different groups. To remedy this problem a streamlined and standardized scRNA-seq data analysis and normalization protocol was designed and developed. After analyzing multiple experiments we identified the possible pipeline stages, and tools needed. Our pipeline is capable of handling data with adapters and barcodes, which was not the case with pipelines from some experiments. Our pipeline can be used to analyze single experiment scRNA-seq data and also to compare scRNA-seq data across experiments. Various processes like data gathering, file conversion, and data merging were automated in the pipeline. The main focus was to standardize and normalize single-cell RNA-seq data to minimize technical noise introduced by disparate platforms.
ContributorsBalachandran, Parithi (Author) / Wang, Xiao (Thesis advisor) / Brafman, David (Committee member) / Lockhart, Thurmon (Committee member) / Arizona State University (Publisher)
Created2017
Description
With more people falling every year it is more important to continue to track everyday activity as well as follow the progress that someone is making over time. As well as at risk subjects, athletes are also wanting to track their activity as well as improve in finer control of their motions and abilities. To improve someone’s balance, strength, flexibility, and more someone can now start to use different biological sensors to help live a healthier and better lifestyle. To build different sensors requires materials that are comfortable to wear and accurate in collecting data. Graphene has been considered a wonder material that is used in many different applications which allow circuits and devices to use the flexible and durable material to conduct electricity. This paper shows multiple different tests and 36 trials of using graphene as a device which measures pressure that can be used to analyze gait patterns. These tests involve walking on a dual force plate treadmill for 90 continuous seconds with the graphene strip in the heel of the shoe wirelessly transmitting data to be recorded. The initial tests show that graphene will pick up noise and that graphene can start to deteriorate without proper protection. When looking at subject 1 there is less than .01 seconds of error between the graphene circuit and the ground truth. The ground truth was collected simultaneously, and the t-tests and ANOVA tests showed that there is no statistical difference between the graphene system and the ground truth. These tests also showed a 96.7% reproducibility score. There are limitations as seen in the later subjects, but these limitations can be overcome by further protecting the graphene and replacing the strip when it starts to show signs of deterioration which will allow graphene to be used in everyday bio wearable devices.
ContributorsSweeten, William (Author) / Lockhart, Thurmon (Thesis advisor) / Arquiza, Jose Apollo (Committee member) / Soangra, Rahul (Committee member) / Arizona State University (Publisher)
Created2022