Controlling Robotic Hands with AI

The video explains how AI assists in controlling robotic hands by learning from a healthy hand's movements. It highlights the complexity of hand movements and how AI speeds up the process of translating these movements to a robotic hand. The mechanism involves using a data glove and sensors to capture muscle movements, which are then processed by a machine learning algorithm to control a robotic hand in real-time.

What is the main challenge in controlling a robotic hand as described in the video?

• The lack of AI technology

• The high cost of robotic hands

• The need for 27 buttons to control it

• The absence of Bluetooth connectivity

How has AI improved the process of adapting movements to a robotic hand?

• By increasing the number of button combinations

• By eliminating the need for a data glove

• By speeding up the learning process

• By reducing the number of sensors needed

What is required to translate movements from a healthy hand to a robotic hand?

• A Bluetooth device and a smartphone

• A video game controller

• A set of 27 buttons

• A data glove and a microchip with sensors

What role do the sensors play in the process of controlling a robotic hand?

• They store data for future use

• They connect the hand to the internet

• They read muscle movements and send signals

• They provide power to the robotic hand

How are the signals from the sensors used in controlling the robotic hand?

• They are used to charge the robotic hand

• They are sent to a smartphone app

• They are stored for later analysis

• They are translated into movements by a machine learning algorithm

What is the final step in the process of moving a robotic hand?

• Calibrating the sensors

• Programming the hand with new movements

• Sending signals via Bluetooth to the hand

• Connecting the hand to a power source