AIMC Topic: Artificial Limbs

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Proof of Concept of an Assistive Robotic Arm Control Using Artificial Stereovision and Eye-Tracking.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Assistive robotic arms have become popular to help users with upper limb disabilities achieve autonomy in their daily tasks, such as drinking and grasping objects in general. Usually, these robotic arms are controlled with an adapted joystick. Joysti...

What microsurgeon, orthopaedic and plastic surgeon should know about bionic hand.

Injury
Hand loss is a catastrophic event that generates significant demands for orthopedics and prosthetics. In the course of history, prostheses evolved from passive esthetic replacements to sophisticated robotic hands. Yet, their actuation and particularl...

Locomotion Mode Recognition With Robotic Transtibial Prosthesis in Inter-Session and Inter-Day Applications.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Locomotion mode recognition across multiple sessions and days is an indispensable step towards the practical use of the robotic transtibial prosthesis. In this study, we proposed an adaptive recognition strategy to against the time-varying features o...

A CNN-Based Method for Intent Recognition Using Inertial Measurement Units and Intelligent Lower Limb Prosthesis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Powered intelligent lower limb prosthesis can actuate the knee and ankle joints, allowing transfemoral amputees to perform seamless transitions between locomotion states with the help of an intent recognition system. However, prior intent recognition...

Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Research on machine learning approaches for upper-limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient's everyday lives remains a challenge because advanced control schemes tend to break do...

A survey on what Australians with upper limb difference want in a prosthesis: justification for using soft robotics and additive manufacturing for customized prosthetic hands.

Disability and rehabilitation. Assistive technology
Upper limb prostheses are part of a rapidly changing market place. Despite development in device design, surveys report low levels of uptake and dissatisfaction with current prosthetic design. In this study, we present the results of a survey conduc...

Deep Learning Movement Intent Decoders Trained With Dataset Aggregation for Prosthetic Limb Control.

IEEE transactions on bio-medical engineering
SIGNIFICANCE: The performance of traditional approaches to decoding movement intent from electromyograms (EMGs) and other biological signals commonly degrade over time. Furthermore, conventional algorithms for training neural network based decoders m...

E-Skin Bimodal Sensors for Robotics and Prosthesis Using PDMS Molds Engraved by Laser.

Sensors (Basel, Switzerland)
Electronic skin (e-skin) is pursued as a key component in robotics and prosthesis to confer them sensing properties that mimic human skin. For pressure monitoring, a great emphasis on piezoresistive sensors was registered due to the simplicity of sen...

Robotic hand illusion with tactile feedback: Unravelling the relative contribution of visuotactile and visuomotor input to the representation of body parts in space.

PloS one
The rubber hand illusion describes a phenomenon in which participants experience a rubber hand as being part of their body by the synchronous application of visuotactile stimulation to the real and the artificial limb. In the recently introduced robo...

Adapting myoelectric control in real-time using a virtual environment.

Journal of neuroengineering and rehabilitation
BACKGROUND: Pattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, a...