Prosthetic hands get identification boost to predict precise grip strength need

Researchers at Guilin University of Electronic Technology in China have developed an advanced prosthetic hand system that integrates vision and machine learning to automate and optimize grip strength. Traditional prosthetics use Electromyography (EMG) sensors to detect a user’s intent to grasp but cannot accurately determine the necessary pressure, forcing users to consciously adjust their grip to avoid crushing or dropping objects. The new system employs a palm-mounted camera combined with pressure sensors on the prosthetic fingertips and EMG signals from the forearm. When the user reaches for an object, the camera identifies it, and a machine learning algorithm references a database of required grip strengths for common items, enabling the prosthetic to apply the appropriate force automatically. This innovation aims to make prosthetic hand use more intuitive by freeing users from the mental burden of calculating grip strength, allowing them to focus on the task itself. The researchers are also working on adding haptic feedback to create a two-way communication system that sends tactile sensations back to the user, enhancing the lif