Using machine learning, a camera, and a sensor, researchers from Guilin University of Electronic Technology in China are improving the way prosthetic hands predict grip strength by guiding appropriate grip strength decisions in real time.
“We want to free the user from thinking about how to control [an object] and allow them to focus on what they want to do, achieving a truly natural and intuitive interaction,” said author Hua Li.
The researchers measured the grip strength prosthesis users needed to interact with common items such as pens, cups and bottles, balls, keys, and eggs and fed the measurements into a machine learning-based object identification system that uses a small camera placed near the palm of the prosthetic hand. The system uses an electromyography (EMG) sensor at the user’s forearm to determine what the user intends to do with the object at hand.
“An EMG signal can clearly convey the intent to grasp, but it struggles to answer the critical question, how much force is needed? This often requires complex training or user calibration,” said Li. “Our approach was to offload that ‘how much’ question to the vision system.”
The group plans to integrate haptic feedback into their system, providing an intuitive physical sensation to the user, which can establish a two-way communication bridge between the user and the hand using additional EMG signals.
“What we are most looking forward to, and currently focused on, is enabling users with prosthetic hands to seamlessly and reliably perform the fine motor tasks of daily living,” said Li. “We hope to see users be able to effortlessly tie their shoelaces or button a shirt, confidently pick up an egg or a glass of water without consciously calculating the force, and naturally peel a piece of fruit or pass a plate to a family member.”
Editor’s note: This story was adapted from materials provided by AIP Publishing.
The open-access study, “Design of intelligent artificial limb hand with force control based on machine vision,” was published in Nanotechnology and Precision Engineering.
