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ARTIFICIAL INTELLIGENCE (AI) ENABLES SENSORIMOTOR INTEGRATION FOR PROSTHETIC HAND DEXTERITY
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.
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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)
Embodied Biological Computers: Closing The Loop on Sensorimotor Integration of Dexterous Robotic Hands
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.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00014092
Subject Headings
Artificial hands, Neuroprostheses, Neurotechnology (Bioengineering), Robotics
Format
Document (PDF)