Using CNNs for increased precision control grasp and grip, and Reinforcement Learning (RL) to reduce the training time needed to design customized prosthetics, increase adaptability with different people, all on a cheap 3D printed prosthetic arm alternative.

Status Quo

There are about 2 million amputees in the U.S. alone, and that number is expected to nearly double to 3.6 million by 2050. However, current prosthetics aren't living up to the demand in many cases and can be significantly improved.

How it works:

Your brain controls the muscles in your limbs by sending electrical commands down the spinal cord and then through peripheral nerves to the muscles. This information pathway would be blocked if you had a limb amputated. The peripheral nerves would still carry electrical motor command signals generated in the brain, but the signals would meet a dead end at the site of amputation and never reach the amputated muscles.

We essentially try to achieve this same thing with a prosthetic arm so that we can still do daily tasks (however, most of them do this without the feeling being returned to the person). Today, when a wearer of a prosthetic arm wants to grab something there are three main ways in which this single is sent:

  1. Grip mechanism might be controlled mechanically = a cable attached to the opposite shoulder.
  2. Signal to grip is detected using myoelectric sensors (these will read muscle activity from the skin).
  3. Sensors to measure nerve signals that are implanted inside the muscle.

1. Body-Powered Mechanical Control

Body-powered prostheses are the most commonly used artificial arm. A body-powered prosthetic arm is held in place by suction or by a strap system that rests on your shoulders. A cable and harness system controls the device.


There are two main types of body-powered hand prostheses: