Handling eggs requires delicacy: Apply too much pressure, and they crack. But to open a water bottle, a huge amount of grip strength and pressure is needed.
There are approximately 50,000 new amputations each year in the United States, according to the U.S. Centers for Disease Control and Prevention. The loss of a hand can be especially challenging, hindering individuals’ capacity to carry out daily activities.
A major user-experience issue with prosthetic hands is the need to accurately adjust the grip strength according to the object being manipulated.
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.
Hua Li, Study Author, School of Life and Environmental Sciences, Guilin University of Electronic Technology
Pens, cups, bottles, balls, metal sheet items such as keys, and delicate objects like eggs constitute more than 90 % of the various items that disabled patients use on a daily basis.
The researchers assessed the grip strength required to handle these everyday items and input these measurements into a machine learning-driven object identification system, which employs a small camera positioned near the palm of the prosthetic hand.
The system incorporates an electromyography (EMG) sensor located on the user’s forearm to ascertain the user’s intended action with the object in their grasp.
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. Our approach was to offload that ‘how much’ question to the vision system.
Hua Li, Study Author, School of Life and Environmental Sciences, Guilin University of Electronic Technology
The team intends to incorporate haptic feedback into their system, offering users an intuitive physical sensation that can create a two-way communication link between the user and the hand through supplementary 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. 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.
Hua Li, Study Author, School of Life and Environmental Sciences, Guilin University of Electronic Technology
Journal Reference:
Li, Y., et al. (2026). An intelligent artificial hand with force control based on machine vision. Nanotechnology and Precision Engineering. DOI: 10.1063/5.0253551. https://pubs.aip.org/tu/npe/article/9/1/013009/3377418/An-intelligent-artificial-hand-with-force-control.