Scientists build robo-glove to help stroke victims recover - study

If they survive a stroke, patients usually need rehabilitation to relearn to walk, talk or perform daily tasks. Research has shown that besides physical and occupational therapy, music therapy can help stroke patients recover language and motor function.

But for people trained in music who suffered a stroke, playing music may itself be a skill that needs to be relearned, as patients with neuromuscular disorders commonly face challenges when it comes to engaging in everyday activities. For instance, after a stroke, their ability to carry out daily tasks can be affected due to decreased coordination and strength in one or both of their upper limbs. In addition, spasticity can develop over time and affect the ability to perform personal hygiene tasks, leading to further deterioration of the affected limb’s function.

Now, a study in Frontiers in Robotics and AI under the title “Feeling the beat: a smart hand exoskeleton for learning to play musical instruments” has shown how novel soft robotics can help recovering patients relearn playing music and other skills that require nimbleness and coordination.

“Here we show that our smart exoskeleton glove – with its integrated tactile sensors, soft actuators and artificial intelligence – can effectively aid in the relearning of manual tasks after neurotrauma,” said lead author Prof. Maohua Lin of the ocean and mechanical engineering department of Florida Atlantic University.

Lin and colleagues designed and tested a “smart hand exoskeleton” in the shape of a multi-layered, flexible 3D-printed robo-glove that weighs only 191 grams. The glove’s entire palm and wrist area is designed to be soft and flexible, and the shape of the glove can be custom-made to fit each wearer’s anatomy.

How does the robotic glove improve the quality of life for its users?

Soft pneumatic actuators in its fingertips generate motion and exert force, thus mimicking natural, fine-tuned hand movements. Each fingertip also contains an array of 16 flexible sensors or “taxels” that give tactile sensations to the wearer’s hand when they interact with objects or surfaces. Production of the glove is simple, as all actuators and sensors are put in place through a single molding process.

“While wearing the glove, human users have control over the movement of each finger to a significant extent,” said Lin’s colleague and senior author Prof. Erik Engeberg. “The glove is designed to assist and enhance their natural hand movements, allowing them to control the flexion and extension of their fingers. The glove supplies hand guidance, providing support and amplifying dexterity,” he commented.

How does the robotic glove improve the quality of life for its users?

The authors believe that, in the future, patients might ultimately wear a pair of these gloves to help both hands independently to regain dexterity, motor skills and a sense of coordination.

The authors used machine learning to successfully teach the glove to “feel” the difference between playing a correct versus incorrect versions of a beginner’s song on the piano; the glove operated autonomously without human input, with preprogrammed movements. The song was “Mary had a little lamb,” which requires four fingers to play.

“We found that the glove can learn to distinguish between correct and incorrect piano play, so it could be a valuable tool for personalized rehabilitation of people who wish to relearn to play music,” said Engeberg.

Now that the proof-of-principle has been shown, the glove can be programmed to give feedback to the wearer about what went right or wrong in their play, either through haptic feedback, visual cues or sound. These would enable wearers to understand their performance and make improvements.

Lin added that adapting the present design to other rehabilitation tasks beyond playing music, for example object manipulation, would require customization to individual needs. This can be facilitated through 3D scanning technology or CT scans to ensure a personalized fit and functionality for each user.”