Since the natural movements of the human hand are highly complex, creating and providing adequately functional tools for people with amputations is a true design challenge. The BrainRobotics hand prosthesis has been created against the background that the familiar metal hooks or static prostheses offer only limited functionality and that robotic prosthetics for one rely on physical buttons or shaking the prosthesis and yet only offer switching between predefined gestures. The comparatively very cost-effective BrainRobotics prosthetic hand incorporates artificial intelligence and individual programming options. This new approach towards functionality is based on electromyography sensors that capture muscle signals from the user’s residual limb and then translate them into movements. The prosthetic hand is thus able to react to and almost intuitively perform the user’s intended movements and gestures. Different gestures only take a few minutes to be programmed. As a result, people with hand amputations no longer need to rely on a limited number of preprogrammed movements. Rather, they can easily customise the movement patterns of the prosthesis according to their individual needs. In addition, the integrated AI employs an algorithm that allows the hand and user to interactively learn from each other to make the movements more lifelike with each use.
Statement by the Jury
This AI-based hand prosthesis fascinates with its integrated intelligent technology. In an innovative manner, it allows wearers to make intuitive movements using the muscle signals from their residual limb. Using artificial intelligence in the processing of those muscle signals allows generating more lifelike movements that do not rely on preprogrammed movement patterns. This opens up new realms of freedom for users in their daily lives.
Credits
Manufacturer:
BrainCo, Somerville, MA, USA
In-house design:
Dr. Molei Wu, Bicheng Han, Junlin Wang, Xiao Li,
Zhaoyi Yang, Di Wu, Junqing Qiao, Colin Worth
Design:
Shenzhen Mental Flow Technology Co., Ltd.,
Wen Sun, Jiao Cheng, Ang Li, Shenzhen, China,
TAG, Stuart Jang, Boston, USA
