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- Creators: Harrington Bioengineering Program
- Resource Type: Text
Description
Bioscience High School, a small magnet high school located in Downtown Phoenix and a STEAM (Science, Technology, Engineering, Arts, Math) focused school, has been pushing to establish a computer science curriculum for all of their students from freshman to senior year. The school's Mision (Mission and Vision) is to: "..provide a rigorous, collaborative, and relevant academic program emphasizing an innovative, problem-based curriculum that develops literacy in the sciences, mathematics, and the arts, thus cultivating critical thinkers, creative problem-solvers, and compassionate citizens, who are able to thrive in our increasingly complex and technological communities." Computational thinking is an important part in developing a future problem solver Bioscience High School is looking to produce. Bioscience High School is unique in the fact that every student has a computer available for him or her to use. Therefore, it makes complete sense for the school to add computer science to their curriculum because one of the school's goals is to be able to utilize their resources to their full potential. However, the school's attempt at computer science integration falls short due to the lack of expertise amongst the math and science teachers. The lack of training and support has postponed the development of the program and they are desperately in need of someone with expertise in the field to help reboot the program. As a result, I've decided to create a course that is focused on teaching students the concepts of computational thinking and its application through Scratch and Arduino programming.
ContributorsLiu, Deming (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
It is unknown which regions of the brain are most or least active for golfers during a peak performance state (Flow State or "The Zone") on the putting green. To address this issue, electroencephalographic (EEG) recordings were taken on 10 elite golfers while they performed a putting drill consisting of hitting nine putts spaced uniformly around a hole each five feet away. Data was collected at three time periods, before, during and after the putt. Galvanic Skin Response (GSR) measurements were also recorded on each subject. Three of the subjects performed a visualization of the same putting drill and their brain waves and GSR were recorded and then compared with their actual performance of the drill. EEG data in the Theta (4 \u2014 7 Hz) bandwidth and Alpha (7 \u2014 13 Hz) bandwidth in 11 different locations across the head were analyzed. Relative power spectrum was used to quantify the data. From the results, it was found that there is a higher magnitude of power in both the theta and alpha bandwidths for a missed putt in comparison to a made putt (p<0.05). It was also found that there is a higher average power in the right hemisphere for made putts. There was not a higher power in the occipital region of the brain nor was there a lower power level in the frontal cortical region during made putts. The hypothesis that there would be a difference between the means of the power level in performance compared to visualization techniques was also supported.
ContributorsCarpenter, Andrea (Co-author) / Hool, Nicholas (Co-author) / Muthuswamy, Jitendran (Thesis director) / Crews, Debbie (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The goal of this project was to explore biomimetics by creating a jellyfish flying device that uses propulsion of air to levitate while utilizing electromyography signals and infrared signals as mechanisms to control the device. Completing this project would require knowledge of biological signals, electrical circuits, computer programming, and physics to accomplish. An EMG sensor was used to obtain processed electrical signals produced from the muscles in the forearm and was then utilized to control the actuation speed of the tentacles. An Arduino microprocessor was used to translate the EMG signals to infrared blinking sequences which would propagate commands through a constructed circuit shield to the infrared receiver on jellyfish. The receiver will then translate the received IR sequence into actions. Then the flying device must produce enough thrust to propel the body upwards. The application of biomimetics would best test my skills as an engineer as well as provide a method of applying what I have learned over the duration of my undergraduate career.
ContributorsTsui, Jessica W (Author) / Muthuswamy, Jitteran (Thesis director) / Blain Christen, Jennifer (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
The global population over the age of 60 is estimated to rise to 23% by 2050 only increase the prevalence of functional neurological disorders and stroke. Increase in cases of functional neurological disorders and strokes will place a greater burden on the healthcare industry, specifically physical therapy. Physical therapy is vital for a patient’s recovery of motor function which is time demanding and taxing on the physical therapist. Wearable robotics have been proven to improve functional outcomes in gait rehabilitation by providing controlled high dosage and high-intensity training. Accurate control strategies for assistive robotic exoskeletons are vital for repetitive high precisions assistance for cerebral plasticity to occur.
This thesis presents a preliminary determination and design of a control algorithm for an assistive ankle device developed by the ASU RISE Laboratory. The assistive ankle device functions by compressing a spring upon heel strike during gait, remaining compressed during mid-stance and then releasing upon initiation of heel-off. The relationship between surface electromyography and ground reactions forces were used for identification of user-initiated heel-off. The muscle activation of the tibialis anterior combined with the ground reaction forces of the heel pressure sensor generated potential features that will be utilized in the revised control algorithm for the assistive ankle device. Work on this project must proceed in order to test and validate the revised control algorithm to determine its accuracy and precision.
This thesis presents a preliminary determination and design of a control algorithm for an assistive ankle device developed by the ASU RISE Laboratory. The assistive ankle device functions by compressing a spring upon heel strike during gait, remaining compressed during mid-stance and then releasing upon initiation of heel-off. The relationship between surface electromyography and ground reactions forces were used for identification of user-initiated heel-off. The muscle activation of the tibialis anterior combined with the ground reaction forces of the heel pressure sensor generated potential features that will be utilized in the revised control algorithm for the assistive ankle device. Work on this project must proceed in order to test and validate the revised control algorithm to determine its accuracy and precision.
ContributorsGaytan-Jenkins, Daniel Rinaldo (Author) / Zhang, Wenlong (Thesis director) / Tyler, Jamie (Committee member) / Harrington Bioengineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Electromyography (EMG) and Electroencephalography (EEG) are techniques used to detect electrical activity produced by the human body. EMG detects electrical activity in the skeletal muscles, while EEG detects electrical activity from the scalp. The purpose of this study is to capture different types of EMG and EEG signals and to determine if the signals can be distinguished between each other and processed into output signals to trigger events in prosthetics. Results from the study suggest that the PSD estimates can be used to compare signals that have significant differences such as the wrist, scalp, and fingers, but it cannot fully distinguish between signals that are closely related, such as two different fingers. The signals that were identified were able to be translated into the physical output simulated on the Arduino circuit.
ContributorsJanis, William Edward (Author) / LaBelle, Jeffrey (Thesis director) / Santello, Marco (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-12
Description
Electromyography (EMG) is an extremely useful tool in extracting control signals from the human body. Needle electromyography is the current standard for obtaining superior quality muscle signals and obtaining signals corresponding to individual muscles. However, needle EMG faces many problems when converting from the laboratory to marketable devices, specifically in home devices. Many patients have issues with needles and the extra care required of needle EMG is prohibitive. Therefore, a surface EMG device that can obtain clear signals from individual muscles would be valuable to many markets in the development of next generation in home devices. Here, signals from surface EMG were analyzed using a low noise EMG evaluation system (RHD 2000; Intan Technologies). The signal to noise ratio (SNR) was calculated using MatLab. The average SNR is 4.447 for the Extensor Carpi Ulnaris, and 7.369 for the Extensor Digitorum Communis. Spectral analysis was performed using the Welch approach in MatLab. The power spectrum indicated that low frequency signals dominate the EMG of small hand muscles. Also, harmonic bands of 60Hz noise were present as part of the signal which should be accounted for with filters in future iterations of the testing method. Provided is evidence that strong, independent signals were acquired and could be used in further application of surface EMG corresponding to lifting of the fingers.
ContributorsSnyder, Joshua Scott (Author) / Muthuswamy, Jit (Thesis director) / Buneo, Christopher (Committee member) / Harrington Bioengineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The cocktail party effect describes the brain’s natural ability to attend to a specific voice or audio source in a crowded room. Researchers have recently attempted to recreate this ability in hearing aid design using brain signals from invasive electrocorticography electrodes. The present study aims to find neural signatures of auditory attention to achieve this same goal with noninvasive electroencephalographic (EEG) methods. Five human participants participated in an auditory attention task. Participants listened to a series of four syllables followed by a fifth syllable (probe syllable). Participants were instructed to indicate whether or not the probe syllable was one of the four syllables played immediately before the probe syllable. Trials of this task were separated into conditions of playing the syllables in silence (Signal) and in background noise (Signal With Noise), and both behavioral and EEG data were recorded. EEG signals were analyzed with event-related potential and time-frequency analysis methods. The behavioral data indicated that participants performed better on the task during the “Signal” condition, which aligns with the challenges demonstrated in the cocktail party effect. The EEG analysis showed that the alpha band’s (9-13 Hz) inter-trial coherence could potentially indicate characteristics of the attended speech signal. These preliminary results suggest that EEG time-frequency analysis has the potential to reveal the neural signatures of auditory attention, which may allow for the design of a noninvasive, EEG-based hearing aid.
ContributorsLaBine, Alyssa (Author) / Daliri, Ayoub (Thesis director) / Chao, Saraching (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / Harrington Bioengineering Program (Contributor)
Created2023-05