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- Creators: Harrington Bioengineering Program
- Resource Type: Text
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