Robotic Hand System
Robotic Hand System - Related Videos
05:30
Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
0 Views •
2025
This study explores the effects of a configurable soft pneumatic robot on enhancing whole-brain network topology post-stroke. Graph theory analysis indicates significant improvements in clustering coefficient, path length, and global efficiency. Findings highlight the potential of programmable robotic protocols to modulate neuroplasticity and optimize functional recovery in stroke rehabilitation.
06:44
Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand
0 Views •
Cited by 8 •
2020
This study reports the development of a novel robot-assisted task-oriented program for hand rehabilitation. The developmental process consists of experiments using both healthy subjects and subjects who have had a stroke and suffered from subsequent motor control dysfunction.
07:34
Functional MRI in Conjunction with a Novel MRI-compatible Hand-induced Robotic Device to Evaluate Rehabilitation of Individuals Recovering from Hand Grip Deficits
0 Views •
Cited by 4 •
2019
We performed functional MRI using a novel MRI-compatible hand-induced robotic device to evaluate its utility for monitoring hand motor function in individuals recovering from neurological deficits.
11:01
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
0 Views •
Cited by 12 •
2015
The overall goal of this method is to establish an SSVEP-based experimental procedure by integrating multiple software programs to enable the study of brain-robot interaction with humanoid robots, which is prospective in assisting the sick and elderly as well as performing unsanitary or dangerous jobs.
08:16
Adaptation of a Haptic Robot in a 3T fMRI
0 Views •
Cited by 2 •
2011
The adaptation and use of a haptic robot in a 3T fMRI is described.
FAQs
Sensors detect motion, force, or muscle activity and provide signals that guide electronic commands. Actuators then convert those commands into movement through mechanical joints and artificial fingers. This coordination allows the hand to perform controlled grasping and positioning rather than producing isolated motions, which is important when supporting purposeful movement during medical care or rehabilitation.
Adjustable assistance allows the system to provide support appropriate to a patient’s movement needs, while measurable feedback shows how the hand responds during practice. Together, these features can help structure repeated purposeful movements and make changes in performance observable. This combination supports more individualized rehabilitation and contributes to research on human motor control.
Artificial fingers provide the structures that interact with objects, while mechanical joints enable coordinated positioning and movement. Electronic commands must direct these components together so the system can reproduce functions such as grasping. Their integration matters medically because useful hand assistance depends on coordinated action across multiple moving parts, not simply on powering a single finger.
The system can use detected motion, force, or muscle activity as sources of control information. These signals are converted into electronic commands that guide movement through the hand’s mechanical components. Using more than one type of detected input supports research and development of technologies tailored to how a person initiates or controls upper-limb movement.
In upper-limb prostheses, the system can support replacement or restoration of hand functions such as grasping and positioning. In assistive devices, it can help a person interact with the environment by providing controlled movement. The same sensor and actuator principles allow developers to adjust how much support the device supplies for a particular user or task.
A rehabilitation session can involve detecting the user’s movement or muscle activity, translating that information into electronic commands, and producing assisted hand movement through actuators. The patient then practices purposeful actions while the system provides adjustable assistance and measurable feedback. This workflow can support repeated interaction with the environment and help inform personalized rehabilitation technologies.