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- All Subjects: Mechanical Engineering
Description
Electromyogram (EMG)-based control interfaces are increasingly used in robot teleoperation, prosthetic devices control and also in controlling robotic exoskeletons. Over the last two decades researchers have come up with a plethora of decoding functions to map myoelectric signals to robot motions. However, this requires a lot of training and validation data sets, while the parameters of the decoding function are specific for each subject. In this thesis we propose a new methodology that doesn't require training and is not user-specific. The main idea is to supplement the decoding functional error with the human ability to learn inverse model of an arbitrary mapping function. We have shown that the subjects gradually learned the control strategy and their learning rates improved. We also worked on identifying an optimized control scheme that would be even more effective and easy to learn for the subjects. Optimization was done by taking into account that muscles act in synergies while performing a motion task. The low-dimensional representation of the neural activity was used to control a two-dimensional task. Results showed that in the case of reduced dimensionality mapping, the subjects were able to learn to control the device in a slower pace, however they were able to reach and retain the same level of controllability. To summarize, we were able to build an EMG-based controller for robot devices that would work for any subject, without any training or decoding function, suggesting human-embedded controllers for robotic devices.
ContributorsAntuvan, Chris Wilson (Author) / Artemiadis, Panagiotis (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Santos, Veronica J (Committee member) / Arizona State University (Publisher)
Created2013
Description
The football helmet is a device used to help mitigate the occurrence of impact-related traumatic (TBI) and minor traumatic brain injuries (mTBI) in the game of American football. The current design methodology of using a hard shell with an energy absorbing liner may be adequate for minimizing TBI, however it has had less effect in minimizing mTBI. The latest research in brain injury mechanisms has established that the current design methodology has produced a helmet to reduce linear acceleration of the head. However, angular accelerations also have an adverse effect on the brain response, and must be investigated as a contributor of brain injury.
To help better understand how the football helmet design features effect the brain response during impact, this research develops a validated football helmet model and couples it with a full LS-DYNA human body model developed by the Global Human Body Modeling Consortium (v4.1.1). The human body model is a conglomeration of several validated models of different sections of the body. Of particular interest for this research is the Wayne State University Head Injury Model for modeling the brain. These human body models were validated using a combination of cadaveric and animal studies. In this study, the football helmet was validated by laboratory testing using drop tests on the crown of the helmet. By coupling the two models into one finite element model, the brain response to impact loads caused by helmet design features can be investigated. In the present research, LS-DYNA is used to study a helmet crown impact with a rigid steel plate so as to obtain the strain-rate, strain, and stress experienced in the corpus callosum, midbrain, and brain stem as these anatomical regions are areas of concern with respect to mTBI.
To help better understand how the football helmet design features effect the brain response during impact, this research develops a validated football helmet model and couples it with a full LS-DYNA human body model developed by the Global Human Body Modeling Consortium (v4.1.1). The human body model is a conglomeration of several validated models of different sections of the body. Of particular interest for this research is the Wayne State University Head Injury Model for modeling the brain. These human body models were validated using a combination of cadaveric and animal studies. In this study, the football helmet was validated by laboratory testing using drop tests on the crown of the helmet. By coupling the two models into one finite element model, the brain response to impact loads caused by helmet design features can be investigated. In the present research, LS-DYNA is used to study a helmet crown impact with a rigid steel plate so as to obtain the strain-rate, strain, and stress experienced in the corpus callosum, midbrain, and brain stem as these anatomical regions are areas of concern with respect to mTBI.
ContributorsDarling, Timothy (Author) / Rajan, Subramaniam D. (Thesis advisor) / Muthuswamy, Jitendran (Thesis advisor) / Oswald, Jay (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2014
Description
There is a tremendous need for wireless biological signals acquisition for the microelectrode-based neural interface to reduce the mechanical impacts introduced by wire-interconnects system. Long wire connections impede the ability to continuously record the neural signal for chronic application from the rodent's brain. Furthermore, connecting and/or disconnecting Omnetics interconnects often introduces mechanical stress which causes blood vessel to rupture and leads to trauma to the brain tissue. Following the initial implantation trauma, glial tissue formation around the microelectrode and may possibly lead to the microelectrode signal degradation. The aim of this project is to design, develop, and test a compact and power efficient integrated system (IS) that is able to (a) wirelessly transmit triggering signal from the computer to the signal generator which supplies voltage waveforms that move the MEMS microelectrodes, (b) wirelessly transmit neural data from the brain to the external computer, and (c) provide an electrical interface for a closed loop control to continuously move the microelectrode till a proper quality of neural signal is achieved. One of the main challenges of this project is the limited data transmission rate of the commercially available wireless system to transmit 400 kbps of digitized neural signals/electrode, which include spikes, local field potential (LFP), and noise. A commercially available Bluetooth module is only capable to transmit at a total of 115 kbps data transfer rate. The approach to this challenge is to digitize the analog neural signal with a lower accuracy ADC to lower the data rate, so that is reasonable to wirelessly transfer neural data of one channel. In addition, due to the limited space and weight bearing capability to the rodent's head, a compact and power efficient integrated system is needed to reduce the packaged volume and power consumption. 3D SoP technology has been used to stack the PCBs in a 3D form-factor, proper routing designs and techniques are implemented to reduce the electrical routing resistances and the parasitic RC delay. It is expected that this 3D design will reduce the power consumption significantly in comparison to the 2D one. The progress of this project is divided into three different phases, which can be outlined as follow: a) Design, develop, and test Bluetooth wireless system to transmit the triggering signal from the computer to the signal generator. The system is designed for three moveable microelectrodes. b) Design, develop, and test Bluetooth wireless system to wirelessly transmit an amplified (200 gain) neural signal from one single electrode to an external computer. c) Design, develop, and test a closed loop control system that continuously moves a microelectrode in searching of an acceptable quality of neural spikes. The outcome of this project can be used not only for the need of neural application but also for a wider and general applications that requires customized signal generations and wireless data transmission.
ContributorsZhou, Li (Author) / Muthuswamy, Jitendran (Thesis advisor) / Sutanto, Jemmy (Thesis advisor) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2012