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A prosthetic that restores the sense of where your hand is

Robohub

Researchers have developed a next-generation bionic hand that allows amputees to regain their proprioception. The results of the study, which have been published in Science Robotics, are the culmination of ten years of robotics research. The next-generation bionic hand, developed by researchers from EPFL, the Sant'Anna School of Advanced Studies in Pisa and the A. Gemelli University Polyclinic in Rome, enables amputees to regain a very subtle, close-to-natural sense of touch. The scientists managed to reproduce the feeling of proprioception, which is our brain's capacity to instantly and accurately sense the position of our limbs during and after movement – even in the dark or with our eyes closed. The new device allows patients to reach out for an object on a table and to ascertain an item's consistency, shape, position and size without having to look at it.


Database of natural movements to feed machine-learning algorithms for prostheses

#artificialintelligence

Most amputees use purely aesthetic prostheses. They find it difficult to accept a robotic limb that is not only by and large complicated to use but also has somewhat unnatural motion. Most of the models on the market today can only execute a few simple gestures, for example opening and closing the fist, and often in a very jarring way. Furthermore, users can't always properly control the magnitude of the movement, which adds a safety risk to the mixture. Scientists are therefore striving to bring prosthetic movements closer to those of the human body by using machine learning, a technique also used in artificial intelligence.


Database of natural movements to feed machine-learning algorithms for prostheses

#artificialintelligence

Most amputees use purely aesthetic prostheses. They find it difficult to accept a robotic limb that is not only by and large complicated to use but also has somewhat unnatural motion. Most of the models on the market today can only execute a few simple gestures, for example opening and closing the fist, and often in a very jarring way. Furthermore, users can't always properly control the magnitude of the movement, which adds a safety risk to the mixture. Scientists are therefore striving to bring prosthetic movements closer to those of the human body by using machine learning, a technique also used in artificial intelligence.