New research has shown how novel soft robotics can help recovering stroke survivors to relearn playing music and other skills that require dexterity and coordination.
Previous research has shown that music therapy can help stroke survivors to recover language and motor function, however, for those trained in music pre-stroke they may need to relearn this skill.
Study lead author, Dr Maohua Lin, says: “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.
If the glove fits
Dr Lin alongside colleagues designed and tested a ‘smart hand exoskeleton’ in the shape of a multi-layered, flexible 3D-printed robo-glove, weighing in at only 191g. The entirety of the palm and wrist area of the glove are designed to be soft and flexible, and the shape of the glove can be custom-made to fit each wearer’s anatomy.
Soft pneumatic actuators in its fingertips are able to generate motion and exert force, allowing the device to mimic natural, fine-tuned hand movements. Each fingertip also contains an array of 16 flexible sensors, which give the user tactile sensations in their hand upon interaction with objects or surfaces.
In means of production, the developers state that the glove is “straightforward” due to all actuators and sensors being put in place using a single moulding process.
Senior author, Dr Erik Engeberg, says: “While wearing the glove, human users have control over the movement of each finger to a significant extent.
“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 believe that patients may want to wear the gloves as a pair, in order to help both hands independently to regain dexterity, motor skills, and a sense of coordination.
Being trained by artificial intelligence
The study authors used machine learning to teach the glove to ‘feel’ the difference between playing correct and incorrect versions of a beginner’s song on the piano. In this instance, the glove operated autonomously without human input, with preprogrammed movements. The song was ‘Mary had a little lamb’, which requires four fingers to play.
Engeberg, says: “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.”
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.
Addressing remaining challenges
Lin added: “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.”
