Learning differences are widely understood as being neurobiological in origin: as such, they remain a long-life condition.
Motor Dysgraphia and Dyslexia are both learning differences where complete diagnosis has to be made by a practitioner qualified to administer the range of standardised assessments required for an accurate diagnosis.
Nevertheless, these learning differences are also characterised by specific difficulties.
The American Psychiatric Association also known as DSM-5 is an authoritative volume providing such characteristics:
These characteristics can be accurately measured by a computer. Such accurate measurements have led the artificial intelligence community to consider that, given enough data, accurate screenings can be performed by:
During the Dyscreen dyslexia screening, we record the person being assessed as they read a list of 32 words. 16 words are real English words and 16 are pseudowords or nonsense words, which are generated by artificial intelligence techniques based on the age of the individual being screened. The audio recordings are processed on our server via specific software and we extract 6 metrics, which are important for the likelihood estimate.
As can be seen in the figure below (which is based on our data), these reading times highlight a clear discrepancy between standard readers and dyslexic readers.
*The unit of time we measure is in milliseconds (ms).
We have trained our machine learning algorithm on data gathered from standard readers and dyslexic readers obtained from our educational and clinical partners. Our machine learning predictor currently provides a likelihood of dyslexia with above 90% accuracy by analysing audio recordings from the screenee.
During the screening, we ask for an image of handwritten text approximately 4 to 5 lines in length. It is recommended our instructions are followed when taking the picture as this will simplify our processing and make our measurement more accurate. We do not check the spelling of the text as it does not seem relevant for screening motor dysgraphia. From the text, we extract 8 features:
Here is an example for slant feature:
We have manually extracted these measurements from every picture we have received (approximately 1500), including examples from standard writers and dysgraphic writers.
Once the writing sample has been submitted, classical processing and machine learning methods are applied to finally provide an algorithm able to: