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Dorsal root ganglia neurite outgrowth measured as a function of changes in microelectrode array resistance
Title
Dorsal root ganglia neurite outgrowth measured as a function of changes in microelectrode array resistance.
Creator
Jordan M. Renna, Jessica M. Stukel, Rebecca Kuntz Willits, Erik D. Engeberg
Abstract/Description
Current research in prosthetic device design aims to mimic natural movements using a feedback system that connects to the patient's own nerves to control the device. The first step in using neurons to control motion is to make and maintain contact between neurons and the feedback sensors. Therefore, the goal of this project was to determine if changes in electrode resistance could be detected when a neuron extended a neurite to contact a sensor. Dorsal root ganglia (DRG) were harvested from...
Show moreCurrent research in prosthetic device design aims to mimic natural movements using a feedback system that connects to the patient's own nerves to control the device. The first step in using neurons to control motion is to make and maintain contact between neurons and the feedback sensors. Therefore, the goal of this project was to determine if changes in electrode resistance could be detected when a neuron extended a neurite to contact a sensor. Dorsal root ganglia (DRG) were harvested from chick embryos and cultured on a collagencoated carbon nanotube microelectrode array for two days. The DRG were seeded along one side of the array so the processes extended across the array, contacting about half of the electrodes. Electrode resistance was measured both prior to culture and after the two day culture period. Phase contrast images of the microelectrode array were taken after two days to visually determine which electrodes were in contact with one or more DRG neurite or tissue. Electrodes in contact with DRG neurites had an average change in resistance of 0.15 MΩ compared with the electrodes without DRG neurites. Using this method, we determined that resistance values can be used as a criterion for identifying electrodes in contact with a DRG neurite. These data are the foundation for future development of an autonomous feedback resistance measurement system to continuously monitor DRG neurite outgrowth at specific spatial locations.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FAUIR000503
Format
Document (PDF)
Feeling the beat: a smart hand exoskeleton for learning to play musical instruments
Title
Feeling the beat: a smart hand exoskeleton for learning to play musical instruments.
Creator
Maohua Lin, Rudy Paul, Moaed Abd, James Jones, Darryl Dieujuste, Harvey Chim, Erik D. Engeberg
Abstract/Description
Individuals who have suffered neurotrauma like a stroke or brachial plexus injury often experience reduced limb functionality. Soft robotic exoskeletons have been successful in assisting rehabilitative treatment and improving activities of daily life but restoring dexterity for tasks such as playing musical instruments has proven challenging. This research presents a soft robotic hand exoskeleton coupled with machine learning algorithms to aid in relearning how to play the piano by ‘feeling’...
Show moreIndividuals who have suffered neurotrauma like a stroke or brachial plexus injury often experience reduced limb functionality. Soft robotic exoskeletons have been successful in assisting rehabilitative treatment and improving activities of daily life but restoring dexterity for tasks such as playing musical instruments has proven challenging. This research presents a soft robotic hand exoskeleton coupled with machine learning algorithms to aid in relearning how to play the piano by ‘feeling’ the difference between correct and incorrect versions of the same song. The exoskeleton features piezoresistive sensor arrays with 16 taxels integrated into each fingertip. The hand exoskeleton was created as a single unit, with polyvinyl acid (PVA) used as a stent and later dissolved to construct the internal pressure chambers for the five individually actuated digits. Ten variations of a song were produced, one that was correct and nine containing rhythmic errors. To classify these song variations, Random Forest (RF), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) algorithms were trained with data from the 80 taxels combined from the tactile sensors in the fingertips. Feeling the differences between correct and incorrect versions of the song was done with the exoskeleton independently and while the exoskeleton was worn by a person. Results demonstrated that the ANN algorithm had the highest classification accuracy of 97.13% ± 2.00% with the human subject and 94.60% ± 1.26% without. These findings highlight the potential of the smart exoskeleton to aid disabled individuals in relearning dexterous tasks like playing musical instruments.
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Date Issued
2023
PURL
http://purl.flvc.org/fau/fd/FAUIR000534
Format
Document (PDF)