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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
One type of assistive device for the blind has attempted to convert visual information into information that can be perceived through another sense, such as touch or hearing. A vibrotactile haptic display assistive device consists of an array of vibrating elements placed against the skin, allowing the blind individual to receive visual information through touch. However, these approaches have two significant technical challenges: large vibration element size and the number of microcontroller pins required for vibration control, both causing excessively low resolution of the device. Here, I propose and investigate a type of high-resolution vibrotactile haptic display which overcomes these challenges by utilizing a ‘microbeam’ as the vibrating element. These microbeams can then be actuated using only one microcontroller pin connected to a speaker or surface transducer. This approach could solve the low-resolution problem currently present in all haptic displays. In this paper, the results of an investigation into the manufacturability of such a device, simulation of the vibrational characteristics, and prototyping and experimental validation of the device concept are presented. The possible reasons of the frequency shift between the result of the forced or free response of beams and the frequency calculated based on a lumped mass approximation are investigated. It is found that one of the important reasons for the frequency shift is the size effect, the dependency of the elastic modulus on the size and kind of material. This size effect on A2 tool steel for Micro-Meso scale cantilever beams for the proposed system is investigated.
ContributorsWi, Daehan (Author) / SODEMANN, ANGELA A (Thesis advisor) / Redkar, Sangram (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2019
Description
With the advent of GPGPU, many applications are being accelerated by using CUDA programing paradigm. We are able to achieve around 10x -100x speedups by simply porting the application on to the GPU and running the parallel chunk of code on its multi cored SIMT (Single instruction multiple thread) architecture. But for optimal performance it is necessary to make sure that all the GPU resources are efficiently used, and the latencies in the application are minimized. For this, it is essential to monitor the Hardware usage of the algorithm and thus diagnose the compute and memory bottlenecks in the implementation. In the following thesis, we will be analyzing the mapping of CUDA implementation of BLIINDS-II algorithm on the underlying GPU hardware, and come up with a Kepler architecture specific solution of using shuffle instruction via CUB library to tackle the two major bottlenecks in the algorithm. Experiments were conducted to convey the advantage of using shuffle instru3ction in algorithm over only using shared memory as a buffer to global memory. With the new implementation of BLIINDS-II algorithm using CUB library, a speedup of around 13.7% was achieved.
ContributorsWadekar, Ameya (Author) / Sohoni, Sohum (Thesis advisor) / Aukes, Daniel (Committee member) / Redkar, Sangram (Committee member) / Arizona State University (Publisher)
Created2017
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
Description
Suction stabilized floats have been implemented into a variety of applications such as supporting wind turbines in off-shore wind farms and for stabilizing cargo ships. This thesis proposes an alternative use for the technology in creating a system of suction stabilized floats equipped with real time location modules to help first responders establish a localized coordinate system to assist in rescues. The floats create a stabilized platform for each anchor module due to the inverse slack tank effect established by the inner water chamber. The design of the float has also been proven to be stable in most cases of amplitudes and frequencies ranging from 0 to 100 except for when the frequency ranges from 23 to 60 Hz for almost all values of the amplitude. The modules in the system form a coordinate grid based off the anchors that can track the location of a tag module within the range of the system using ultra-wideband communications. This method of location identification allows responders to use the system in GPS denied environments. The system can be accessed through an Android app with Bluetooth communications in close ranges or through internet of things (IoT) using a module as a listener, a Raspberry Pi and an internet source. The system has proven to identify the location of the tag in moderate ranges with an approximate accuracy of the tag location being 15 cm.
ContributorsDye, Michaela (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2020
Description
This work endeavors to lay a solid foundation for the exploration and the considerations of exoskeletons, exosuits, and medical devices concerning proprioceptive feedback. This investigation is situated at the nexus of engineering, neuroscience, and rehabilitation medicine, striving to cultivate a holistic understanding of how mechanical augmentation, interfaced synergistically with human proprioception, can foster enhanced mobility and safety. This is especially pertinent for individuals with compromised motor functions.British Neurologist Oliver Wolf Sacks in 1985 published “The Man who Mistook His Wife for a Hat” a series of his most memorable neurological case describing the brain's strangest pathways. One of these cases is “The Disembodied Lady”, Christina a 27-year-old woman that lost entirely the sense of proprioception due to polyneuropathy. This caused her to not be able to control her body, and she declares that “I feel the wind on my arms and face, and then I know, faintly, I have arms and a face. It’s not the real thing, but it’s something—it lifts this horrible, dead veil for a while. ” Finally, she was able to control her body using vision alone. Dr. Sacks introduced, for the first time, the importance of proprioception, as the sense of position of body parts relative to other parts of the body, to western culture. This document’s mission is to identify unexplored concepts in the literature regarding exoskeletons, wearables and assistive technology and a user’s proprioception, embodiment and utilization when wearing devices. Dr. Philipp Beckerle suggests the need to research the connections between wearable hardware and human sense of proprioception. He also emphasizes the need for functional assessment protocols for wearables devices and the role of embodiment. He criticizes the current commercially available upper-limb prostheses since they only restore limited functions and therefore impede embodiment.
This document’s goal is to identify operative solutions through the adaptation of existing technologies and to use effective solutions to improve the quality of life of people suffering from pathologies or traumatic injuries.
ContributorsVignola, Claudio (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / McDaniels, Troy (Committee member) / Arizona State University (Publisher)
Created2023