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- All Subjects: engineering
- Creators: Sugar, Thomas
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
The purpose of this study is to collect baseline internal and external pressure data for the three most commonly used pelvic circumferential compression devices (PCCD). Unstable pelvic fractures as a result of automobile accidents, falls, and other traumatic injuries mortality rate [3]. Early use of pelvic circumferential compression devices can mitigate fatal outcomes [4]-[5]. Prolonged eternal pressure above 9.3kPa can result in long-term soft tissue damage and pressure ulcers [7]. This study hypothesizes that the application of the three most commonly used PCCDs would result in the same mean maximum point pressure exertion. To study this, internal and external, both analog and digital, pressure apparati were used to collect data. The results of this data collection demonstrate a discrepancy in the pressure distribution between right and left greater trochanters within each PCCD. Additionally, the results suggest there is an effect of internal packing on the pressure exertion externally at the two greater trochanters within each PCCD. Lastly, the differences in pressure exertion between each PCCD, internally and externally, were inconclusive as some compared metrics resulted in statistically significant results while others did not. The methodologies employed in this study can be improved through fixation of pressure collection instruments, utilization of digital pressure mats, and removal of confounding factors. The results of this study indicate that digitized, discrete data over a fixed time interval may be clinically useful, suggesting that a digital data collection would yield more reliable data. Additionally, internally mounted pressure sensor data will provide more precise results than the analog method employed herein, as well as provide insight towards bone reduction and displacement following the application of PCCDs. Finally, the information gathered from this study can be utilized to improve upon existing technologies to create a more innovative solution.
The purpose of this study is to collect baseline internal and external pressure data for the three most commonly used pelvic circumferential compression devices (PCCD). Unstable pelvic fractures as a result of automobile accidents, falls, and other traumatic injuries mortality rate [3]. Early use of pelvic circumferential compression devices can mitigate fatal outcomes [4]-[5]. Prolonged eternal pressure above 9.3kPa can result in long-term soft tissue damage and pressure ulcers [7]. This study hypothesizes that the application of the three most commonly used PCCDs would result in the same mean maximum point pressure exertion. To study this, internal and external, both analog and digital, pressure apparati were used to collect data. The results of this data collection demonstrate a discrepancy in the pressure distribution between right and left greater trochanters within each PCCD. Additionally, the results suggest there is an effect of internal packing on the pressure exertion externally at the two greater trochanters within each PCCD. Lastly, the differences in pressure exertion between each PCCD, internally and externally, were inconclusive as some compared metrics resulted in statistically significant results while others did not. The methodologies employed in this study can be improved through fixation of pressure collection instruments, utilization of digital pressure mats, and removal of confounding factors. The results of this study indicate that digitized, discrete data over a fixed time interval may be clinically useful, suggesting that a digital data collection would yield more reliable data. Additionally, internally mounted pressure sensor data will provide more precise results than the analog method employed herein, as well as provide insight towards bone reduction and displacement following the application of PCCDs. Finally, the information gathered from this study can be utilized to improve upon existing technologies to create a more innovative solution.
Cornhole, traditionally seen as tailgate entertainment, has rapidly risen in popularity since the launching of the American Cornhole League in 2016. However, it lacks robust quality control over large tournaments, since many of the matches are scored and refereed by the players themselves. In the past, there have been issues where entire competition brackets have had to be scrapped and replayed because scores were not handled correctly. The sport is in need of a supplementary scoring solution that can provide quality control and accuracy over large matches where there aren’t enough referees present to score games. Drawing from the ACL regulations as well as personal experience and testimony from ACL Pro players, a list of requirements was generated for a potential automatic scoring system. Then, a market analysis of existing scoring solutions was done, and it found that there are no solutions on the market that can automatically score a cornhole game. Using the problem requirements and previous attempts to solve the scoring problem, a list of concepts was generated and evaluated against each other to determine which scoring system design should be developed. After determining that the chosen concept was the best way to approach the problem, the problem requirements and cornhole rules were further refined into a set of physical assumptions and constraints about the game itself. This informed the choice, structure, and implementation of the algorithms that score the bags. The prototype concept was tested on their own, and areas of improvement were found. Lastly, based on the results of the tests and what was learned from the engineering process, a roadmap was set out for the future development of the automatic scoring system into a full, market-ready product.