Matching Items (17)
Filtering by
- All Subjects: engineering
- Creators: Sugar, Thomas
- Resource Type: Text
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
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.
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.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
The sensor industry is a growing industry that has been predicted by Allied Market Research to be a multi-billion industry by 2022. One of the many key drives behind this rapid growth in the sensor industry is the increase incorporation of sensors into portable electrical devices. The value for sensor technologies are increased when the sensors are developed into innovative measuring system for application uses in the Aerospace, Defense, and Healthcare industries. While sensors are not new, their increased performance, size reduction, and decrease in cost has opened the door for innovative sensor combination for portable devices that could be worn or easily moved around. With this opportunity for further development of sensor use through concept engineering development, three concept projects for possible innovative portable devices was undertaken in this research. One project was the development of a pulse oximeter devise with fingerprint recognition. The second project was prototyping a portable Bluetooth strain gage monitoring system. The third project involved sensors being incorporated onto flexible printed circuit board (PCB) for improved comfort of wearable devices. All these systems were successfully tested in lab.
ContributorsNichols, Kevin William (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Brad (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent research and study have showed the potential of auto-parametric system in controlling stability and parametric resonance. In this project, two different designs for auto-parametrically excited mass-spring-damper systems were studied. The theoretical models were developed to describe the behavior of the systems, and simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results showed that the implementation of auto-parametric system could help reduce or amplify the resonance significantly.
ContributorsLe, Thao (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Brad (Committee member) / Arizona State University (Publisher)
Created2018
Description
Many industries require workers in warehouse and stockroom environments to perform frequent lifting tasks. Over time these repeated tasks can lead to excess strain on the worker's body and reduced productivity. This project seeks to develop an exoskeletal wrist fixture to be used in conjunction with a powered exoskeleton arm to aid workers performing box lifting types of tasks. Existing products aimed at improving worker comfort and productivity typically employ either fully powered exoskeleton suits or utilize minimally powered spring arms and/or fixtures. These designs either reduce stress to the user's body through powered arms and grippers operated via handheld controls which have limited functionality, or they use a more minimal setup that reduces some load, but exposes the user's hands and wrists to injury by directing support to the forearm. The design proposed here seeks to strike a balance between size, weight, and power requirements and also proposes a novel wrist exoskeleton design which minimizes stress on the user's wrists by directly interfacing with the object to be picked up. The design of the wrist exoskeleton was approached through initially selecting degrees of freedom and a ROM (range of motion) to accommodate. Feel and functionality were improved through an iterative prototyping process which yielded two primary designs. A novel "clip-in" method was proposed to allow the user to easily attach and detach from the exoskeleton. Designs utilized a contact surface intended to be used with dry fibrillary adhesives to maximize exoskeleton grip. Two final designs, which used two pivots in opposite kinematic order, were constructed and tested to determine the best kinematic layout. The best design had two prototypes created to be worn with passive test arms that attached to the user though a specially designed belt.
ContributorsGreason, Kenneth Berend (Author) / Sugar, Thomas (Thesis director) / Holgate, Matthew (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
For the past two decades, advanced Limb Gait Simulators and Exoskeletons have been developed to improve walking rehabilitation. A Limb Gait Simulator is used to analyze the human step cycle and/or assist a user walking on a treadmill. Most modern limb gait simulators, such as ALEX, have proven themselves effective and reliable through their usage of motors, springs, cables, elastics, pneumatics and reaction loads. These mechanisms apply internal forces and reaction loads to the body. On the other hand, external forces are those caused by an external agent outside the system such as air, water, or magnets. A design for an exoskeleton using external forces has seldom been attempted by researchers. This thesis project focuses on the development of a Limb Gait Simulator based on a Pure External Force and has proven its effectiveness in generating torque on the human leg. The external force is generated through air propulsion using an Electric Ducted Fan (EDF) motor. Such a motor is typically used for remote control airplanes, but their applications can go beyond this. The objective of this research is to generate torque on the human leg through the control of the EDF engines thrust and the opening/closing of the reverse thruster flaps. This device qualifies as "assist as needed"; the user is entirely in control of how much assistance he or she may want. Static thrust values for the EDF engine are recorded using a thrust test stand. The product of the thrust (N) and the distance on the thigh (m) is the resulting torque. With the motor running at maximum RPM, the highest torque value reached was that of 3.93 (Nm). The motor EDF motor is powered by a 6S 5000 mAh LiPo battery. This torque value could be increased with the usage of a second battery connected in series, but this comes at a price. The designed limb gait simulator demonstrates that external forces, such as air, could have potential in the development of future rehabilitation devices.
ContributorsToulouse, Tanguy Nathan (Author) / Sugar, Thomas (Thesis director) / Artemiadis, Panagiotis (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
A control method based on the phase angle is used to control oscillating systems. The phase oscillator uses the sine and cosine of the phase angle to change key properties of a mass-spring-damper system, including amplitude, frequency, and equilibrium. An inverted pendulum is used to show a further application of the phase oscillator. Two methods of control based on the phase oscillator are used for swing-up and balancing of the pendulum. The first control method involves two separate stages. The scenarios where this control works are discussed. The second control method uses variable coefficients to result in a smooth transition between swing-up and balancing.
ContributorsBates, Andrew (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2015
Description
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.
ContributorsGillespie, Reagan (Author) / Sugar, Thomas (Thesis director) / Li, Baoxin (Committee member) / Barrett, The Honors College (Contributor) / Engineering Programs (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2023-05
Description
As the world moves towards faster production times, quicker shipping, and overall, more demanding schedules, the humans caught in the loop are subject to physical duress causing them to physically break down and have muscular skeletal injuries. Surprisingly, with more automation in logistics houses, the remaining workers must be quicker and do more, again resulting in muscular-skeletal injuries. To help alleviate this strain, a class of robotics and wearables has arisen wherein the human is assisted by a worn mechanical device. These devices, traditionally called exoskeletons, fall into two general categories: passive and active. Passive exoskeletons employ no electronics to activate their assistance and instead typically rely on the spring-like qualities of many materials. These are generally lighter weight than their active counterparts, but also lack the assistive power and can even interfere in other routine operations. Active exoskeletons, on the other hand, aim to avoid as much interference as possible by using electronics and power to assist the wearer. Properly executed, this can deliver power at the most opportune time and disengage from interference when not needed. However, if the tuning is mismatched from the human, it can unintentionally increase loads and possibly lead to other future injuries or harm. This dissertation investigates exoskeleton technology from two vantage points: the designer and the consumer. In the first, the creation of the Aerial Porter Exoskeleton (APEx) for the US Air Force (USAF). Testing of this first of its kind exoskeleton revealed a peak metabolic savings of 8.13% as it delivers 30 N-m of torque about each hip. It was tested extensively in live field conditions over 8 weeks to great success. The second section is an exploration of different commercially available exoskeletons and the development of a common set of standards/testing protocols is described. The results show a starting point for a set of standards to be used in a rapidly growing sector.
ContributorsMartin, William Brandon (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Thesis advisor) / Hollander, Kevin (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the extensive technological progress made in the areas of drives, sensors and processing, exoskeletons and other wearable devices have become more feasible. However, the stringent requirements in regards to size and weight continue to exert a strong influence on the system-wide design of these devices and present many obstacles to a successful solution. On the other hand, while the area of controls has seen a significant amount of progress, there also remains a large potential for improvements. This dissertation approaches the design and control of wearable devices from a systems perspective and provides a framework to successfully overcome the often-encountered obstacles with optimal solutions. The electronics, drive and control system design for the HeSA hip exoskeleton project and APEx hip exoskeleton project are presented as examples of how this framework is used to design wearable devices. In the area of control algorithms, a real-time implementation of the Fast Fourier Transform (FFT) is presented as an alternative approach to extracting amplitude and frequency information of a time varying signal. In comparison to the peak search method (PSM), the FFT allows extracting basic gait signal information at a faster rate because time windows can be chosen to be less than the fundamental gait frequency. The FFT is implemented on a 16-bit processor and the results show the real-time detection of amplitude and frequency coefficients at an update rate of 50Hz. Finally, a novel neural networks based approach to detecting human gait activities is presented. Existing neural networks often require vast amounts of data along with significant computer resources. Using Neural Ordinary Differential Equations (Neural ODEs) it is possible to distinguish between seven different daily activities using a significantly smaller data set, lower system resources and a time window of only 0.1 seconds.
ContributorsBoehler, Alexander (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Hollander, Kevin (Committee member) / Arizona State University (Publisher)
Created2021
Description
There has been a decrease in the fertility rate over the years due to today’s younger generation facing more pressure in the workplace and their personal lives. With an aging population, more and more older people with limited mobility will require nursing care for their daily activities. There are several applications for wearable sensor networks presented in this paper. The study will also present a motion capture system using inertial measurement units (IMUs) and a pressure-sensing insole with a control system for gait assistance using wearable sensors. This presentation will provide details on the implementation and calibration of the pressure-sensitive insole, the IMU-based motion capture system, as well as the hip exoskeleton robot. Furthermore, the estimation of the Ground Reaction Force (GRF) from the insole design and implementation of the motion tracking using quaternion will be discussed in this document.
ContributorsLi, Xunguang (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Subramanian, Susheelkumar (Committee member) / Arizona State University (Publisher)
Created2022