Robotics and artificial intelligence (AI) are giving folks who have had Neuro trauma hope. An AI-powered soft robotic glove can aid people with neuro muscular problems in regaining piano skills, according to new research in the journal Frontiers. AI Robotic Glove Could Restore Piano Playing for Stroke Victims.
AI Robotic Glove Could Restore Piano Playing for Stroke Victims
Other soft robotic piano players have been employed in the past, but according to the study’s authors from Florida Atlantic University, Boise State University, and the University of Florida College of Medicine, “ours is the only one that hasdemonstrated the capability to ‘feel’ the difference between correct and incorrect versions of the same song.”
Injury to the brain or spinal cord, often known as neurotrauma, is a major global health issue. The World Health Organization (WHO) estimates that 69 million people worldwide experience a traumatic brain injury (TBI) each year. According to the World Stroke Organization (WSO), 12.2 million new strokes are thought to occur annually around the world, and one in every four persons over the age of 25 will experience a stroke at some point in their lifetime. According to the WSO, 101 million people worldwide—a figure that has nearly doubled over the past 30 years—live with the effects of a stroke. AI Robotic Glove Could Restore Piano Playing for Stroke Victims
The researchers outfitted a soft robotic exoskeleton with piezoresistive sensor arrays with 16 taxels for five fingertips.
Piezoresistive sensors measure pressure with a high degree of accuracy. Their name reflects the piezoresistive effect, which is the change in electrical resistance when stress or strain is applied mechanically. Taxels (TActile piXEL) are sensors that can recognize contact pressure by calculating how much force is being applied to an area.
The researchers produced 10 song variations of “Mary Had a Little Lamb” (one correct and nine with rhythmic errors) and trained random forest (RF), K-nearest neighbor (KNN), and artificial neural network (ANN) algorithms with data collected from the five fingertip sensors. AI Robotic Glove Could Restore Piano Playing for Stroke Victims
A random forest algorithm, also known as a random decision forest, is a type of user-friendly machine learning algorithm that uses supervised machine learning. In artificial intelligence, supervised machine learning refers to a technique that uses labeled input data to train an algorithm to make predictions or classify data. These algorithms are widely used for classification and regression tasks. Instead of using just one decision tree, random forest algorithms consist of many individual decision trees that work together as an ensemble, hence the name “forest.” Class predictions from each decision tree are made and the one with the majority of votes becomes the final output. AI Robotic Glove Could Restore Piano Playing for Stroke Victims
K-nearest neighbor algorithms are also popular supervised machine learning algorithms that are commonly used for classification and regression problems. This basic algorithm stores all available cases instead of performing calculations, and then performs classification based on similarity. It is also considered a non-parametric method because the algorithm does not have assumptions about the data distribution. The algorithm looks at nearest annotated data point, or nearest neighbor, in order to classify a data point, hence the algorithm’s name. AI Robotic Glove Could Restore Piano Playing for Stroke Victims
A user-friendly machine learning algorithm that makes use of supervised machine learning is the random forest algorithm, also known as the random decision forest. In artificial intelligence, the term “supervised machine learning” refers to a method that trains an algorithm to generate predictions or categorize data using labeled input data. For tasks involving classification and regression, these methods are frequently utilized. The name “forest” refers to the ensemble of many distinct decision trees that make up random forest algorithms rather than the use of a single decision tree. Each decision tree generates a set of class predictions, and the one that receives the most votes is used as the final result. AI Robotic Glove Could Restore Piano Playing for Stroke Victims