New glove free VR tech could transform rehab

Developed by a team at Zhengzhou University, the system integrates deep learning with ionic hydrogel electrodes to recognise hand gestures based on electromyographic (EMG) signals.

Conventional hand rehabilitation therapy often relies on bulky mechanical gloves that can increase the strain on a patient’s hand. These devices are also complex to operate and often require specialised medical facilities.

The newly developed system eliminates the need for heavy, hand-worn equipment, offering a load-free and flexible rehabilitation solution. Patients can engage in rehabilitation exercises anywhere and anytime, without having to wear such cumbersome devices.

The system’s ionic hydrogel electrodes are wet-adhesive, self-healing, and conductive. These electrodes, applied directly to the forearm, collect EMG signals generated by hand movements. Convolutional Neural Networks (CNNs) then process these signals to recognise a range of hand gestures.

In a trial, the system is said to have achieved 97.9 per cent accuracy in recognising 14 different Jebsen hand rehabilitation gestures, a standardised and objective measure of fine and gross motor hand functions that uses simulated activities of daily living.