Current Search: Engeberg, Erik D. (x)
View All Items
- 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.
Show less - Date Issued
- 2017
- PURL
- http://purl.flvc.org/fau/fd/FAUIR000503
- Format
- Document (PDF)
- Title
- Embodied Biological Computers: Closing The Loop on Sensorimotor Integration of Dexterous Robotic Hands.
- Creator
- Ades, Craig, Engeberg, Erik D., Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
- Abstract/Description
-
The sensation of touch is an integral part of using our hands. As different researchers work toward the restoration of afferent sensation in prosthetic hands, it becomes urgent to better understand how an artificial hand’s afferent inputs are affected by the efferent muscular outputs, and vice-versa. Current methods of neuroprosthetic research have many regulatory hurdles, time, cost, and associated risk to the patient. To circumvent these hurdles, we developed a non-invasive, closed-loop (CL...
Show moreThe sensation of touch is an integral part of using our hands. As different researchers work toward the restoration of afferent sensation in prosthetic hands, it becomes urgent to better understand how an artificial hand’s afferent inputs are affected by the efferent muscular outputs, and vice-versa. Current methods of neuroprosthetic research have many regulatory hurdles, time, cost, and associated risk to the patient. To circumvent these hurdles, we developed a non-invasive, closed-loop (CL) neuroprosthetic research platform, integrating artificial tactile signals from an artificial hand with biomimetically-stimulated biological neuronal networks (BNNs) cultured in a multielectrode array (MEA) chamber. These living embodied biological computers (EBCs) can provide a non-invasive alternative for investigating invasive neuroprosthetic interfaces. With them we can explore a variety of control techniques, tactile sensation encoding methods, and neural decoding methods to increase the rate of research in this area with minimal regulatory approval, greatly reduced cost and time, and no risk to the patients. In the first stage of this integration, our EBC was programmed to embody neuronal spiking from spontaneously active “efferent” receptive fields in cultured BNNs as intentional signals for movement. Bursts were transferred to a robotic hand and initiated a tapping motion of the index finger laid in proximity to a surface. Contact elicited artificial sensations, which were registered by a biotac tactile sensor array fit to the robotic fingertip.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014092
- Subject Headings
- Artificial hands, Neuroprostheses, Neurotechnology (Bioengineering), Robotics
- Format
- Document (PDF)
- 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.
Show less - Date Issued
- 2023
- PURL
- http://purl.flvc.org/fau/fd/FAUIR000534
- Format
- Document (PDF)
- Title
- ARTIFICIAL INTELLIGENCE (AI) ENABLES SENSORIMOTOR INTEGRATION FOR PROSTHETIC HAND DEXTERITY.
- Creator
- Abd, Moaed A., Engeberg, Erik D., Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
- Abstract/Description
-
Hand amputation is a devastating feeling for amputees, and it is lifestyle changing since it is challenging to perform the basic life activities with amputation. Hand amputation means interrupting the closed loop between sensory feedback and motor control. The absence of sensory feedback requires a significant cognitive effort from the amputee to perform basic daily activities with prosthetic hand. Loss of tactile sensations is a major roadblock preventing amputees from multitasking or using...
Show moreHand amputation is a devastating feeling for amputees, and it is lifestyle changing since it is challenging to perform the basic life activities with amputation. Hand amputation means interrupting the closed loop between sensory feedback and motor control. The absence of sensory feedback requires a significant cognitive effort from the amputee to perform basic daily activities with prosthetic hand. Loss of tactile sensations is a major roadblock preventing amputees from multitasking or using the full dexterity of their prosthetic hands. One of the most significant features lacking from commercial prosthetic hands is sensory feedback, according to amputees. Many amputees abandoned their prosthetic devices due to the lack of tactile feedback. In the field of prosthetics, restoring sensory feedback is the most challenging task due to the complexity of integration between the prosthetic and the peripheral nervous system. A prosthetic hand with sensory feedback that imitates the intact hand would improve the lives of millions of amputees worldwide by inducing the prosthetic hand to be a part of the body image and significant impact the control of the prosthetic. To restore the sensory feedback and improve the dexterity for upper limb amputee, multiple components needed to be integrated together to provide the sensory feedback. Tactile sensors are the first components that needed to be integrated into the sensorimotor loop. In this research two tactile sensors were integrated in the sensory feedback loop. The first tactile sensor is BioTac which is a commercially available sensor. The first novel contribution with BioTac is the development of an ANN classifier to detect the direction a grasped object slips in a dexterous robotic hand in real time, and the second novel aspect of this study is the use of slip direction detection for adaptive robotic grasp reflexes. The second tactile sensor is the liquid metal sensor (LMS), this sensor was developed entirely in our lab (BioRobotics lab). The novel contribution for LMS is to detect and prevent slip in real time application, and to recognize different surface features and different sliding speeds.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00013875
- Subject Headings
- Artificial intelligence, Haptic devices, Tactile sensors, Sensorimotor integration, Artificial hands
- Format
- Document (PDF)