Prosthetic Hand Control
Prosthetic Hand Control - Related Videos
06:58
A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Cited by 34 •
2015
As prosthetic development moves towards the goal of natural control, harnessing amputees’ inherent ability to learn new motor skills may enable proficiency. This manuscript describes a structured rehabilitation protocol, which includes imitation, repetition, and reinforcement learning strategies, for improved multifunctional prosthetic control.
01:31
Hand Control During Infancy and Toddlerhood
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2026
Hand movements during infancy and toddlerhood develop from broad, relatively uncontrolled actions into increasingly precise fine motor skills. This progression depends on nervous system maturation, improved muscle control, and repeated interaction with objects. Early reflexive movements gradually give way to intentional reaching, grasping, object manipulation, and tool use.Early Grasping SkillsNewborns show a strong palmar grasp reflex, automatically closing their fingers when the palm is...
08:04
Customizing a Cryolite Glass Prosthetic Eye
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Cited by 12 •
2019
This manuscript shows each step of customizing a cryolite glass prosthetic eye including some major advantages of the use of cryolite glass for manufacturing an eye prosthesis compared to poly(methyl methacrylate). In addition, this manuscript gives ophthalmologists better insight into the ocularistic care that could improve interprofessional collaboration.
11:16
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
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Cited by 15 •
2014
Neural-machine interfaces (NMI) have been developed to identify the user's locomotion mode. These NMIs are potentially useful for neural control of powered artificial legs, but have not been fully demonstrated. This paper presented (1) our designed engineering platform for easy implementation and development of neural control for powered lower limb prostheses and (2) an experimental setup and protocol in a laboratory environment to evaluate neurally-controlled artificial legs on patients with...
11:00
Hand Controlled Manipulation of Single Molecules via a Scanning Probe Microscope with a 3D Virtual Reality Interface
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Cited by 6 •
2016
We demonstrate the precise manipulation of individual organic molecules on a metal surface with the tip of a scanning probe microscope driven in 3D by the experimenter's hand using a motion capture system and fully immersive virtual reality goggles.
FAQs
The algorithm serves as the link between variable muscle activity and a selected hand action. It interprets electrical signals recorded from residual muscles, then issues commands that coordinate motors controlling the fingers or wrist. This translation matters because the user’s physical intention must become consistent device behavior rather than a direct mechanical movement.
Residual-muscle activity gives the system access to signals associated with the user’s intended movement, but reliable operation depends on how the user learns to produce and interpret those signals. Attention and adaptation therefore become behavioral variables, helping explain why control can improve as users develop more consistent operating strategies.
Position and force sensors can add information that the user’s muscle signals alone do not provide. Position feedback indicates how the artificial hand is configured, whereas force feedback concerns interaction strength. Including these signals can support more informed control and more natural interaction, especially when coordinated finger or wrist actions matter.
Motor learning is important because users must develop reliable strategies for operating the device, rather than simply receiving a movement command once. Researchers can examine how attention and adaptation change during this learning process. These behavioral measures connect control performance with rehabilitation and with the user’s developing relationship to the prosthesis.
A behavior-focused study can follow a sequence of intended movement, device response, and user adjustment. Researchers examine how residual-muscle activity is translated into action, whether feedback about position or force is available, and how the user’s strategy changes over time. This workflow links device operation to motor learning, attention, and adaptation.
Applications extend beyond moving individual fingers. Improved control may support rehabilitation and daily activities while also contributing to embodiment, the user’s sense of a more natural relationship with the artificial hand. In behavioral research, these outcomes show how assistive technology can influence action, attention, and adaptation during everyday interaction.