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The robot hand works with a series of sensors that are attached to the base of the arm under the elbow

Incredible images show a groundbreaking & # 39; hand & # 39; helping a man to grab a bottle and give himself a cup of water, while scientists prove that the life-changing gadget can work for amputees

  • Hand works using a series of sensors that are attached to the base of the arm or stump
  • They send signals to advanced gadgets, giving users control over every finger
  • It uses machine learning to familiarize itself with the arm movements of the amputee
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This is the extraordinary moment that a man could pick up a water bottle and pour himself a drink remotely with an advanced robot hand.

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The gadget works with a series of sensors that are attached to the base of the arm or stump, in the case of amputees.

They read muscle movements and send signals to the prosthetic hand, giving users control over every finger of the machine and allowing them to grab and pick up items.

It is hoped that the technology, which responds to user movements within 0.4 seconds, could end the daily struggle of millions of amputees.

The robot hand works with a series of sensors that are attached to the base of the arm under the elbow

The robot hand works with a series of sensors that are attached to the base of the arm under the elbow

Equipped with pressure sensors along the fingers, the robot hand can react and stabilize an object before the brain realizes that it is slipping
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Equipped with pressure sensors along the fingers, the robot hand can react and stabilize an object before the brain realizes that it is slipping

Equipped with pressure sensors along the fingers, the robot hand can react and stabilize an object before the brain realizes that it is slipping

Professor Aude Billard, one of the researchers behind the design at the EPFL research center in Lausanne, Switzerland, is behind the design

Professor Aude Billard, one of the researchers behind the design at the EPFL research center in Lausanne, Switzerland, is behind the design

Professor Aude Billard, one of the researchers behind the design at the EPFL research center in Lausanne, Switzerland, is behind the design

The prosthetic hand uses machine learning to become familiar with the muscle movements of the user.

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Amputees must perform a number of hand gestures to train the algorithm.

Sensors on the stump of the amputee detect muscle activity, and the algorithm learns which hand movements match which patterns of muscle activity.

Once the intended finger movements of the user have been understood, this information is used to control individual fingers of the prosthetic hand.

Behind the design are researchers from the EPFL research center in Lausanne, Switzerland.

They successfully tested the robot hand on three amputees and seven healthy participants.

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The research team wrote in the study that the technology combines & # 39; two concepts from two different neuroprosthetic areas & # 39 ;.

One of the amputees operates a virtual robot hand during the investigation while the sensors are connected to his stump

One of the amputees operates a virtual robot hand during the investigation while the sensors are connected to his stump

One of the amputees operates a virtual robot hand during the investigation while the sensors are connected to his stump

Arm sensors send signals to the prosthetic hand, giving users control over each individual finger of the machine, as well as the ability to grab and pick up items

Arm sensors send signals to the prosthetic hand, giving users control over each individual finger of the machine, as well as the ability to grab and pick up items

Arm sensors send signals to the prosthetic hand, giving users control over each individual finger of the machine, as well as the ability to grab and pick up items

Main author Katie Zhuang poses by hand, who can respond to user movements in a fraction of a second
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Main author Katie Zhuang poses by hand, who can respond to user movements in a fraction of a second

Main author Katie Zhuang poses by hand, who can respond to user movements in a fraction of a second

They added: "One concept of neuro-engineering involves deciphering the intended finger movement of muscle activity on the stump of the amputee for individual finger control of the prosthetic hand that has never been done before.

"The other, from robotics, allows the robotic hand to grasp objects and keep in touch with them for robust grasping."

Equipped with pressure sensors along the fingers, the robot hand can react and stabilize an object before the brain realizes that it is slipping.

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Aude Billard, leader of EPFL's Learning Algorithms and Systems Laboratory, said: "If you hold an object in your hand and it starts to slip, you only have a few milliseconds to respond. The robot hand can respond within 400 milliseconds. & # 39;

The results are published in the journal Nature Machine Intelligence.

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