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Find video protocols related to scientific articles indexed in Pubmed.
Materials and optimized designs for human-machine interfaces via epidermal electronics.
Adv. Mater. Weinheim
PUBLISHED: 04-29-2013
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Thin, soft, and elastic electronics with physical properties well matched to the epidermis can be conformally and robustly integrated with the skin. Materials and optimized designs for such devices are presented for surface electromyography (sEMG). The findings enable sEMG from wide ranging areas of the body. The measurements have quality sufficient for advanced forms of human-machine interface.
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Prediction of distal arm joint angles from EMG and shoulder orientation for prosthesis control.
Conf Proc IEEE Eng Med Biol Soc
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Current state-of-the-art upper limb myoelectric prostheses are limited by only being able to control a single degree of freedom at a time. However, recent studies have separately shown that the joint angles corresponding to shoulder orientation and upper arm EMG can predict the joint angles corresponding to elbow flexion/extension and forearm pronation/ supination, which would allow for simultaneous control over both degrees of freedom. In this preliminary study, we show that the combination of both upper arm EMG and shoulder joint angles may predict the distal arm joint angles better than each set of inputs alone. Also, with the advent of surgical techniques like targeted muscle reinnervation, which allows a person with an amputation intuitive muscular control over his or her prosthetic, our results suggest that including a set of EMG electrodes around the forearm increases performance when compared to upper arm EMG and shoulder orientation. We used a Time-Delayed Adaptive Neural Network to predict distal arm joint angles. Our results show that our networks root mean square error (RMSE) decreases and coefficient of determination (R(2)) increases when combining both shoulder orientation and EMG as inputs.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.