Exoskeleton glove helps stroke survivors find their rhythm

After a stroke, patients commonly 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 to recover language and motor function. For musicians who suffered a stroke, playing music may itself be a skill that needs to be relearned.

“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 Dr Maohua Lin, an adjunct professor at the Department of Ocean & Mechanical Engineering of Florida Atlantic University, and lead author of a paper detailing the work in Frontiers in Robotics and AI. 

Lin and colleagues designed and tested a ‘smart hand exoskeleton’ in the shape of a multi-layered, flexible 3D-printed robo-glove, which weighs 191g. The palm and wrist areas are designed to be soft and flexible, and the shape of the glove can be custom-made to fit each wearer.

Soft pneumatic actuators in its fingertips generate motion and exert force, which mimics natural, fine-tuned hand movements. Each fingertip contains an array of 16 flexible sensors (‘taxels’) that give tactile sensations to the wearer’s hand when interacting with objects or surfaces. Production of the glove is claimed to be straightforward, as all actuators and sensors are put in place through a single moulding process.

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“While wearing the glove, human users have control over the movement of each finger to a significant extent,” said senior author Dr Erik Engeberg, a professor at Florida Atlantic University’s Department of Ocean & Mechanical Engineering. “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.”

The authors predict that patients might eventually wear a pair of these gloves to help both hands independently regain dexterity, motor skills, and a sense of coordination.

The authors used machine learning to teach the glove to ‘feel’ the difference between playing a correct versus incorrect version of a beginner’s song (‘Mary had a little lamb’) on the piano. Here, the glove operated autonomously without human input, with pre-programmed movements.

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

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 the musician to understand their performance and make improvements.

Lin said: “Adapting the present design to other rehabilitation tasks beyond playing music, for example object manipulation, would require customisation to individual needs. This can be facilitated through 3D scanning technology or CT scans to ensure a personalised fit and functionality for each user.”

“But several challenges in this field need to be overcome. These include improving the accuracy and reliability of tactile sensing, enhancing the adaptability and dexterity of the exoskeleton design, and refining the machine learning algorithms to better interpret and respond to user input.”