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Artificial Limbs

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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...

On the robustness of real-time myoelectric control investigations: a multiday Fitts' law approach.

Journal of neural engineering
OBJECTIVE: Real-time myoelectric experimental protocol is considered as a means to quantify usability of myoelectric control schemes. While usability should be considered over time to assure clinical robustness, all real-time studies reported thus fa...

Control within a virtual environment is correlated to functional outcomes when using a physical prosthesis.

Journal of neuroengineering and rehabilitation
BACKGROUND: Advances such as targeted muscle reinnervation and pattern recognition control may provide improved control of upper limb myoelectric prostheses, but evaluating user function remains challenging. Virtual environments are cost-effective an...

PCA and deep learning based myoelectric grasping control of a prosthetic hand.

Biomedical engineering online
BACKGROUND: For the functional control of prosthetic hand, it is insufficient to obtain only the motion pattern information. As far as practicality is concerned, the control of the prosthetic hand force is indispensable. The application value of pros...

Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques.

Sensors (Basel, Switzerland)
Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myoelectric control for upper limb prostheses with respect to current clinical approaches based on direct control. However, the choice of features for cl...

Improving internal model strength and performance of prosthetic hands using augmented feedback.

Journal of neuroengineering and rehabilitation
BACKGROUND: The loss of an arm presents a substantial challenge for upper limb amputees when performing activities of daily living. Myoelectric prosthetic devices partially replace lost hand functions; however, lack of sensory feedback and strong und...

HapPro: A Wearable Haptic Device for Proprioceptive Feedback.

IEEE transactions on bio-medical engineering
OBJECTIVE: Myoelectric hand prostheses have reached a considerable technological level and gained an increasing attention in assistive robotics. However, their abandonment rate remains high, with unintuitive control and lack of sensory feedback being...