Robotic Glove: Transforming the Lives of Stroke Survivors
The Challenges Faced by Stroke Survivors
Imagine losing the ability to play an instrument you once loved due to a stroke. For stroke survivors, everyday tasks that involve coordination and strength in the upper extremities become incredibly challenging. As a result, researchers at Florida Atlantic University’s College of Engineering and Computer Science have developed a groundbreaking solution – a robotic glove that is revolutionizing the lives of pianists who have suffered a disabling stroke.
Introducing the Soft Robotic Hand Exoskeleton
The soft robotic hand exoskeleton is the first of its kind and combines artificial intelligence with cutting-edge technology to enhance hand dexterity. This remarkable invention utilizes flexible touch sensors, smooth actuators, and AI algorithms to differentiate between correct and incorrect versions of the same song. By providing gentle support and assistance, stroke survivors can regain their motor skills and relearn complex movements required for playing the piano.
The Power of Tactile Sensors
The success of the robotic glove lies in its ability to feel and respond to users’ movements in real-time. The researchers embedded special sensor arrays into each fingertip of the glove, enabling precise force and guidance for retrieving fine finger movements needed to play the piano. With this technology, stroke survivors can receive immediate feedback and make necessary adjustments, facilitating a faster and more effective learning process.
The team at Florida Atlantic University programmed the robotic glove to sense the difference between correct and incorrect versions of the well-known tune “Mary Had a Little Lamb.” By introducing variations in playing, such as early or late timing errors and persisting for different durations, they trained algorithms to classify these variations accurately. The results were astounding, with the Artificial Neural Network algorithm achieving a classification accuracy of 97.13% with a human subject and 94.60% without a human subject. These findings highlight the tremendous potential of the robotic glove in assisting disabled individuals in relearning dexterous tasks such as playing musical instruments.
The Unique Design of the Robotic Glove
One of the key features of the robotic glove is its innovative design. The researchers utilized 3D-printed polyvinyl acid stents and hydrogel casting to integrate five actuators into a single wearable device that conforms to the user’s hand. This manufacturing process allows for customization to the unique anatomy of individual patients, enhancing comfort and usability.
A significant advantage of this glove is its simplicity compared to other designs in the market. By combining all the actuators and sensors in a single molding process, the researchers have created a straightforward yet highly effective solution. Furthermore, the glove’s versatility extends beyond piano playing, making it applicable to various daily living tasks.
Personalized Rehabilitation Programs
Stroke survivors require personalized rehabilitation programs that address their specific weaknesses and help them regain lost functionality. Using the data collected from the robotic glove, clinicians can develop customized action plans based on each patient’s performance. By identifying the sections of the song that are consistently played incorrectly, therapists can determine which motor functions require improvement.
A game-like progression can be implemented, where patients are prescribed more challenging songs as they progress. This approach not only keeps patients engaged but also provides a customizable path to improvement. With the robotic glove’s ability to precisely analyze keystrokes and identify timing errors, stroke survivors can track their progress and focus on specific areas that need further development.
The Future Implications
The development of the soft robotic hand exoskeleton marks a significant milestone in assisting individuals with neuromuscular disorders and reduced limb functionality. This groundbreaking technology opens doors for stroke survivors to rediscover their musical abilities and regain independence.
While the focus of this research has been on piano playing, the possibilities for utilizing the robotic glove in other activities of daily living are immense. With proper customization and integration into rehabilitation programs, stroke survivors can enhance their motor skills, improving their overall quality of life.
Conclusion
The robotic glove developed by researchers at Florida Atlantic University’s College of Engineering and Computer Science has proven to be a game-changer for stroke survivors. Its sophisticated design, combined with AI algorithms and tactile sensors, allows for precise force and guidance during piano playing. By relearning and recovering their motor skills, individuals can regain their independence and engage in activities they once thought were lost.
As further advancements are made in the field of soft robotics, we can anticipate even more innovative solutions that will transform the lives of individuals with neurotrauma. The future holds tremendous potential for restoring functionality and empowering those affected by strokes to live life to the fullest.
Summary:
The development of a soft robotic hand exoskeleton, known as the robotic glove, is revolutionizing the lives of stroke survivors. This groundbreaking technology combines artificial intelligence, tactile sensors, and precise force and guidance to help individuals relearn complex movements required for playing the piano. By differentiating between correct and incorrect versions of a song, stroke survivors can receive real-time feedback and make necessary adjustments, facilitating a faster and more effective learning process. The glove’s innovative design, customization to individual patients’ anatomy, and potential for personalized rehabilitation programs make it a game-changer in assisting individuals with neuromuscular disorders. With further advancements in soft robotics, we can expect even more transformative solutions that restore functionality and empower those affected by strokes to live life to the fullest.
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For people who have suffered neurotrauma such as a stroke, everyday tasks can be extremely challenging due to decreased coordination and strength in one or both upper extremities. These problems have stimulated the development of robotic devices to help improve their capabilities. However, the rigid nature of these assistive devices can be problematic, especially for more complex tasks like playing a musical instrument.
A robotic glove, the first of its kind, is lending a ‘hand’ and hope to pianists who have suffered a disabling stroke. Developed by researchers at Florida Atlantic University’s College of Engineering and Computer Science, the soft robotic hand exoskeleton uses artificial intelligence to enhance hand dexterity.
Combining flexible touch sensors, smooth 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-handed exoskeleton.
“Playing the piano requires complex and highly skilled movements, and relearning tasks involve restoration and retraining of specific movements or skills,” said Erik Engeberg, Ph.D., lead author, professor in the Department of Mechanical and Ocean Engineering. from FAU 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 comprised of soft, flexible materials and sensors that provide gentle support and assistance to individuals in relearning and recovering their motor skills.”
The researchers embedded special sensor arrays into each fingertip of the robotic glove. Unlike previous exoskeletons, this new technology provides precise force and guidance to retrieve the fine finger movements needed to play the piano. By monitoring and responding to users’ movements, the robotic glove offers real-time feedback and adjustments, making it easier for users to understand correct movement techniques.
To demonstrate the robotic glove’s capabilities, the researchers programmed it to sense 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 playing, they created a group of 12 different types of errors that could occur at the beginning or end of a note, or due to early or late timing errors, and persist 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 without errors.
To classify the song variations, the Random Forest (RF), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) algorithms were trained on data from tactile sensors in the fingertips. Feeling the differences between the correct and incorrect versions of the song was done with the robotic glove both independently and while being worn by a person. The accuracy of these algorithms in classifying correct and incorrect song variations with and without the human subject was compared.
Results of the study, published in the journal Frontiers in robotics and AI, showed that the ANN algorithm had the highest classification accuracy of 97.13% with the human subject and 94.60% without the human subject. The algorithm successfully determined the error percentage for a given song, as well as identifying keystrokes that were out of time. These findings highlight the potential of the smart robotic glove to help disabled people relearn dexterous tasks such as playing musical instruments.
The 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 manufacturing process is new and the form factor can 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, since all the actuators and sensors are combined in a single molding process,” Engeberg said. “Importantly, although the application in this study was to play a song, the approach could be applied to myriad tasks of daily living, and the device could facilitate intricate rehabilitation programs customized for each patient.”
Clinicians could use the data to develop personalized action plans to identify a patient’s weaknesses, which can present as sections of the song that are constantly playing wrongly and can be used to determine which motor functions require improvement. As patients progress, the rehabilitation team could prescribe more challenging songs 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 game changer for people with neuromuscular disorders and reduced limb functionality,” said Stella Batalama, Ph.D., dean of the College of Engineering and Science of Computing of the FAU. “Although other soft robotic actuators have been used for piano playing, our robotic glove is the only one that has demonstrated the ability to ‘feel’ the difference between correct and incorrect versions of the same song.”
Study co-authors are Maohua Lin, first author and Ph.D. student; Rudy Paul, graduate student; and Moaed Abd, Ph.D., a recent graduate; all from FAU’s College of Engineering and Computer Science; James Jones, Boise State University; Darryl Dieujuste, graduate research assistant in FAU’s College of Engineering and Computer Science; and Harvey Chim, MD, 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 NIH National Institute on Aging, and the National Science Foundation. This research was supported in part by a seed grant from FAU’s College of Engineering and Computer Science and FAU’s Institute of Sensing and Integrated Network Systems Engineering (I-SENSE).
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