• Robotic glove that 'feels' lends a 'hand

    From ScienceDaily@1:317/3 to All on Fri Jun 30 22:30:28 2023
    Robotic glove that 'feels' lends a 'hand' to relearn playing piano after
    a stroke

    Date:
    June 30, 2023
    Source:
    Florida Atlantic University
    Summary:
    A new soft robotic glove is lending a 'hand' and providing hope
    to piano players who have suffered a disabling stroke or other
    neurotrauma.

    Combining flexible tactile sensors, soft actuators and AI, this
    robotic glove is the first to 'feel' the difference between
    correct and incorrect versions of the same song and to combine
    these features into a single hand exoskeleton. Unlike prior
    exoskeletons, this new technology provides precise force and
    guidance in recovering the fine finger movements required for
    piano playing and other complex tasks.


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    FULL STORY ==========================================================================
    For people who have suffered neurotrauma such as a stroke, everyday
    tasks can be extremely challenging because of decreased coordination
    and strength in one or both upper limbs. These problems have spurred the development of robotic devices to help enhance their abilities. However,
    the rigid nature of these assistive devices can be problematic, especially
    for more complex tasks like playing a musical instrument.

    A first-of-its-kind robotic glove is lending a "hand" and providing
    hope to piano players who have suffered a disabling stroke. Developed
    by researchers from Florida Atlantic University's College of Engineering
    and Computer Science, the soft robotic hand exoskeleton uses artificial intelligence to improve hand dexterity.

    Combining flexible tactile sensors, soft actuators and AI, this robotic
    glove is the first to "feel" the difference between correct and incorrect versions of the same song and to combine these features into a single
    hand exoskeleton.

    "Playing the piano requires complex and highly skilled movements, and relearning tasks involves the restoration and retraining of specific
    movements or skills," said Erik Engeberg, Ph.D., senior author,
    a professor in FAU's Department of Ocean and Mechanical Engineering
    within the College of Engineering and Computer Science, and a member
    of the FAU Center for Complex Systems and Brain Sciences and the FAU Stiles-Nicholson Brain Institute. "Our robotic glove is composed of
    soft, flexible materials and sensors that provide gentle support and
    assistance to individuals to relearn and regain their motor abilities." Researchers integrated special sensor arrays into each fingertip of the
    robotic glove. Unlike prior exoskeletons, this new technology provides
    precise force and guidance in recovering the fine finger movements
    required for piano playing. By monitoring and responding to users'
    movements, the robotic glove offers real-time feedback and adjustments,
    making it easier for them to grasp the correct movement techniques.

    To demonstrate the robotic glove's capabilities, researchers programmed
    it to feel the difference between correct and incorrect versions of
    the well-known tune, "Mary Had a Little Lamb," played on the piano. To introduce variations in the performance, they created a pool of 12
    different types of errors that could occur at the beginning or end of
    a note, or due to timing errors that were either premature or delayed,
    and that persisted for 0.1, 0.2 or 0.3 seconds.

    Ten different song variations consisted of three groups of three
    variations each, plus the correct song played with no errors.

    To classify the song variations, Random Forest (RF), K-Nearest Neighbor
    (KNN) and Artificial Neural Network (ANN) algorithms were trained with
    data from the tactile sensors in the fingertips. Feeling the differences between correct and incorrect versions of the song was done with the
    robotic glove independently and while worn by a person. The accuracy of
    these algorithms was compared to classify the correct and incorrect song variations with and without the human subject.

    Results of the study, published in the journal Frontiers in Robotics and AI,demonstrated that the ANN algorithm had the highest classification
    accuracy of 97.13 percent with the human subject and 94.60 percent without
    the human subject. The algorithm successfully determined the percentage
    error of a certain song as well as identified key presses that were out
    of time. These findings highlight the potential of the smart robotic
    glove to aid individuals who are disabled to relearn dexterous tasks
    like playing musical instruments.

    Researchers designed the robotic glove using 3D printed polyvinyl acid
    stents and hydrogel casting to integrate five actuators into a single
    wearable device that conforms to the user's hand. The fabrication process
    is new, and the form factor could be customized to the unique anatomy
    of individual patients with the use of 3D scanning technology or CT scans.

    "Our design is significantly simpler than most designs as all the
    actuators and sensors are combined into a single molding process,"
    said Engeberg.

    "Importantly, although this study's application was for playing a song,
    the approach could be applied to myriad tasks of daily life and the
    device could facilitate intricate rehabilitation programs customized for
    each patient." Clinicians could use the data to develop personalized
    action plans to pinpoint patient weaknesses, which may present themselves
    as sections of the song that are consistently played erroneously and
    can be used to determine which motor functions require improvement. As
    patients progress, more challenging songs could be prescribed by the rehabilitation team in a game-like progression to provide a customizable
    path to improvement.

    "The technology developed by professor Engeberg and the research team
    is truly a gamechanger for individuals with neuromuscular disorders
    and reduced limb functionality," said Stella Batalama, Ph.D., dean of
    the FAU College of Engineering and Computer Science. "Although other
    soft robotic actuators have been used to play the piano; our robotic
    glove is the only one that has demonstrated the capability to 'feel'
    the difference between correct and incorrect versions of the same song."
    Study co-authors are Maohua Lin, first author and a Ph.D. student; Rudy
    Paul, a graduate student; and Moaed Abd, Ph.D., a recent graduate; all
    from the FAU College of Engineering and Computer Science; James Jones,
    Boise State University; Darryl Dieujuste, a graduate research assistant,
    FAU College of Engineering and Computer Science; and Harvey Chim, M.D.,
    a professor in the Division of Plastic and Reconstructive Surgery at
    the University of Florida.

    This research was supported by the National Institute of Biomedical
    Imaging and Bioengineering of the National Institutes of Health (NIH),
    the National Institute of Aging of the NIH and the National Science
    Foundation. This research was supported in part by a seed grant from the
    FAU College of Engineering and Computer Science and the FAU Institute
    for Sensing and Embedded Network Systems Engineering (I-SENSE).

    * RELATED_TOPICS
    o Health_&_Medicine
    # Disability # Bladder_Disorders # Today's_Healthcare
    o Mind_&_Brain
    # Brain-Computer_Interfaces # Music # Stroke
    o Matter_&_Energy
    # Acoustics # Wearable_Technology # Engineering
    o Computers_&_Math
    # Robotics # Neural_Interfaces # Artificial_Intelligence
    * RELATED_TERMS
    o Left-handed o Virtual_reality o Robot o
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    ========================================================================== Story Source: Materials provided by Florida_Atlantic_University. Original written by Gisele Galoustian. Note: Content may be edited for style
    and length.


    ========================================================================== Journal Reference:
    1. Maohua Lin, Rudy Paul, Moaed Abd, James Jones, Darryl Dieujuste,
    Harvey
    Chim, Erik D. Engeberg. Feeling the beat: a smart hand exoskeleton
    for learning to play musical instruments. Frontiers in Robotics
    and AI, 2023; 10 DOI: 10.3389/frobt.2023.1212768 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2023/06/230630130152.htm

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