Stroke, also known as stroke, is one of the leading causes of disability in adults in the European Union. Each year approximately 1.1 million people are affected by this condition.
After suffering a stroke, many patients require rehabilitation to regain motor and language skills. In addition to physical and occupational therapy, music therapy has been shown to be an effective tool in the recovery of stroke patients.
Soft robotics as support in the rehabilitation of patients
A recent study published in Frontiers in Robotics and AI, has revealed how soft robotics can help stroke patients recover by allowing them to relearn skills that require dexterity and coordination, such as playing music. The smart exoskeleton glove developed by the research team has proven to be an effective tool in this process.
The smart exoskeleton glove designed by the research team is a layered 3D printed roba-lucid glove, weighing only 191g. Its flexible and adaptable design allows it to adjust to the anatomy of each user. The glove features smooth pneumatic actuators on the fingertips, which mimic the natural movements of the hands. Additionally, it contains an array of 16 flexible touch sensors on each fingertip, providing tactile sensations to the user during interaction with objects or surfaces.
The researchers used machine learning techniques to teach the glove to differentiate between a correct and incorrect interpretation of a piano song. By using pre-programmed movements, the glove operated autonomously, without the need for human intervention. This demonstrates the potential of the glove as a tool for the personalized rehabilitation of people who wish to relearn to play music.
Feedback, performance improvements and future perspectives
Once proven to work, the glove can be programmed to provide feedback to the user on its performance. This can be accomplished through haptic feedback, visual cues, or sound cues. In this way, the user can understand which aspects of his interpretation were successful and in which aspects he needs to improve.
Although the smart exoskeleton glove shows promising results, there are still challenges to overcome. Among them are improving the accuracy of touch sensing, increasing the adaptability of the exoskeleton design, and refining machine learning algorithms to more effectively interpret and respond to user input. In addition, the possibility of adapting the glove design to other rehabilitation tasks, such as the manipulation of objects, is raised.
