Science
History of Eye Tracking
Early observations of eye movements and gaze behaviour date back to the late 19th century, representing the foundation of eye tracking history. While the technology and methods were rudimentary by modern standards, these early observations laid the groundwork for the development of more advanced eye tracking techniques. In 1879, the French ophthalmologist Louis Émile Javal noticed, for the first time, that readers’ eyes do not skim fluently through the text while reading, but make quick movements mixed with short pauses. These studies were based on naked-eye observations in the absence of a more advanced technology...
Our Technology
Innodem, a company led by a team of neurologists, neuroscientists, AI experts, data scientists, and software engineers, has patented its newly developed technology in North America, Europe, and Asia. The company’s ongoing pipeline of clinical trials in various neurodegenerative disorders provides well-phenotyped patient data that is ingested by various machine learning algorithms to train them to improve predictability models.
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A gaze mapping biomarker, or GMB, is a quantitative measure of human cognition that analyzes the gaze patterns produced while performing short visual tasks. GMBs can help assess cognitive function such as attention, processing speed, working memory, and response inhibition. GMBs have shown that they can aid in disease tracking.
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An eye movement biomarker, or EMB, is a quantitative measure of eye movements that can assist to diagnose or monitor various neurological disorders. Some examples of EMBs are saccades, anti-saccades, pursuit, velocity, latency and amplitude.
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Clinical Trials
This study aims to develop and validate a sensitive and non-invasive eye-tracking software application.
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This study aims to develop and validate a sensitive and non-invasive eye-tracking software application.
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This study aims to develop and validate a sensitive and non-invasive eye-tracking software application.
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The overarching goal of this research protocol is to acquire eye-tracking, cognitive, and disease-severity metrics in multiple sclerosis (MS) patients to further build up a database of MS patients and train a machine learning classifying algorithms to identify which eye-tracking metrics-or combination thereof-can serve as reliable markers of MS disease severity and cognitive status.
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This study aims to develop and validate a sensitive and non-invasive eye-tracking software application.
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