Multifunctional Prosthesis
Multifunctional Prosthesis - 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.
08:08
Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
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Cited by 35 •
2014
Body segmental inertial properties are required for inverse dynamics modeling. Using an oscillation and reaction board technique, inertial properties of below-knee prostheses were measured. Using direct measures of prosthesis inertia in the inverse dynamics model of the prosthetic leg resulted in lower magnitudes of resultant joint forces and moments.
08:46
A Standardized Method for Measuring Internal Lung Surface Area via Mouse Pneumonectomy and Prosthesis Implantation
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Cited by 22 •
2017
Internal lung surface area (ISA) is a critical criterion for assessing lung morphology and physiology in lung diseases and injury-induced alveolar regeneration. We describe here a standardized method that can minimize the measurement bias for ISA in both lung pneumonectomy and prosthesis implantation mouse models.
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...
08:04
Multifunctional Hybrid Fe2O3-Au Nanoparticles for Efficient Plasmonic Heating
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Cited by 17 •
2016
We describe the synthesis and properties of multifunctional Fe2O3-Au nanoparticles produced by a wet chemical approach and investigate their photothermal properties using laser irradiation. The composite Fe2O3-Au nanoparticles retain the properties of both materials, creating a multifunctional structure with excellent magnetic and plasmonic properties.
FAQs
The control system interprets residual muscle activity or other physiological signals as indicators of intended movement. It then directs actuators and mechanical structures to produce the selected prosthetic action. Because the device supports multiple movements or interactions, coordination between sensing, signal interpretation, and actuation is essential for translating changing user intentions into useful behavior.
Each component contributes a different part of the interaction. Sensors provide information about user intent or system conditions, the control system interprets that information, and actuators generate movement through the mechanical structure. Their integration allows the device to respond to physiological signals rather than relying only on passive replacement, supporting more coordinated movement and interaction.
Control develops through adaptation and learning. Users adjust their motor strategies as they discover how physiological signals produce prosthetic actions and how the device responds during tasks. Sensory feedback can guide this adjustment by providing information about interaction or movement. Studying these changes helps behavior researchers examine how people acquire effective human-machine control.
Its distinguishing feature is the coordination of more than one movement or interaction within the same assistive system. This broader capability requires users to adapt their control strategies across actions rather than repeat a single response. In behavior research, that distinction makes it possible to study flexible motor learning, task switching, and responses to different forms of prosthetic feedback.
A behavioral study can examine how a person generates physiological signals, how the control system translates them into prosthetic actions, and how performance changes with practice or feedback. Researchers can then evaluate motor strategies, learning, and task performance across supported movements or interactions. This workflow connects device operation with observable changes in user behavior.
Evaluation can focus on changes in mobility, grasping, and broader task performance, along with the motor strategies users develop. Researchers may also examine how effectively people interpret sensory feedback and learn control. These outcomes show whether the system supports useful action while providing evidence about adaptation and the relationship between human behavior and assistive technology.
They provide a setting for investigating how people adapt movement, use feedback, and learn to operate a human-machine interface. The device links physiological signals with coordinated assistive actions, allowing behavior to be studied during practical interactions. Findings can inform more responsive rehabilitation approaches and guide development of prostheses that better support mobility, grasping, and task performance.
Research on these systems can reveal which motor strategies users develop, how feedback influences learning, and how control changes during task performance. Those observations can guide rehabilitation approaches that account for adaptation rather than treating movement as fixed. They also support the design of more responsive human-machine interfaces that better align prosthetic actions with user intent.