This UAE-made AI tool brings hope to stroke, paralysis patients who can’t speak

Two graduates from a university in Dubai have developed a groundbreaking AI solution designed to help individuals with verbal impairments — suffering from stroke or paralysis — communicate more effectively. Their innovative project features an avatar-based artificial intelligence (AI) model that serves as a digital voice for users, translating their intentions into clear and expressive speech.

This AI avatar acts as a bridge in communication, enabling users to interact more naturally and inclusively. By offering a personalised, user-friendly experience, it has the potential to transform assistive technology in healthcare, creating new possibilities for inclusion and accessibility.

The creators, electrical engineering graduates Abdallah Al-Serkal and Ahmad Yousef, dedicated two years to developing and refining this technology during their undergraduate studies.

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Al-Serkal, now a Master’s student at the Rochester Institute of Technology (RIT) Dubai, explained in an interview with Khaleej Times how the technology captures brain and eye signals through electro-encephalography (EEG) and electro-oculography (EOG) technology. “We use these methods to translate brain signals and eye movements into language, providing a new way for people with speech-related disabilities to communicate,” he said.

He further explained, “EEG can detect brain biosignals. By integrating EEG with AI, we identified how the brain was thinking differently, when for example, a person is thinking of the numbers zero or one. We identified how certain thought patterns correspond to specific numbers or ideas. This concept began as a simple project and evolved over a summer of development.”

How it works

The words are articulated through an interactive avatar, which dynamically delivers the user’s thoughts through textual or visual expression.

Explaining the background to the innovation, Al-Serkal shared that the project originally started as a lab assignment, which they expanded under the encouragement of their professor. The aim was to address communication barriers faced by stroke survivors and patients suffering from paralysis to enhance their quality of life.

The prototype development involved data collection from 50 participants of different ages and backgrounds. “We captured the micro neural (tiny, rapid changes in neuron activity triggered by specific stimuli, such as light, sound, or touch) and eye movements that occur when participants think about different words. The test vocabulary focused on words related to patient needs, such as help, pain, medication, and sleep. Using machine learning, we translated these biosignals into words and validated the results, which were then conveyed through our AI avatar.”

However, before a patient can use it efficiently, the individual needs to undergo training where the patient needs to view and think about specific words.

90% accuracy rate

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