Quantitative Handwriting Assessment Tool for Healthy and Impaired Children

No technology currently exists for objective assessment of handwritingeven though overmillion individuals in the United States suffer from disorders that affect handwritingincludingmillion children with dyspraxia and overolder adults with Parkinsonandapos s diseaseThis lack of assessment technology creates a barrier to effective carebecause occupational therapists cannot easily distinguish motor disorders from nonmotor causes of handwriting disabilitya necessary step toward developing appropriate treatment plansOur goal is to develop Write to Handthe first quantitative digital assessment of the motor skills that underlie handwritingIn partnership with Washington University in StLouisPlatformSTL is uniquely suited to address the handwriting assessment gapOur team is led by a neuroscientist who developed the Precision Drawing Taska laboratory research tool with an established history of successful assessment and training of writing related motor skillsThe Precision Drawing Task will serve as the basis for Write to HandOur Phase I objective is to develop a fully functional prototype of Write to Handand demonstrate scientific validity in children gradesData analysis will include an initial application of a machine learning approachwhich will test its feasibility in advance of its primary role in Phase IIAimof this STTR is to develop the Write to Hand iPad appwhich must meet seven specific criteria includinghigh precision data collection with a fine point stylusrapid calculation of movement speedsmoothnessstraightnessand error rateand data anonymization that meets HIPAA and IRB standardsAimwill demonstrate validity by comparing Write to Hand performance against a handwriting benchmark inchildren gradesWe expect that movement smoothness will significantly and meaningfullyrandgtpredict handwriting skilland we will use a machine learning approachGeneralized Factorial Methoddetermine the most predictive classifier for handwriting skillidentify task features that optimize handwriting predictionand demonstrate feasibility of our machine learning approach to characterize and classify Write to Hand dataOur product will transform therapy for individuals with handwriting disabilitiesby providing educatorstherapistsand researchers with a gold standard for assessment and quantification of handwritingandapos s underlying motor control skillsThis willfor the first timeallow objective identification of motor impairment in local and tele health settingsIn Phase II we will collect data across participant ages to train our machine learning algorithm so Write to Hand can label performance with easy to interpret grade level ratingsOur commercialization plan focuses on occupational therapists and educators who serve children with motor disabilities Therapists currently have no objective tools to measure handwriting in children or impaired adultsThis project will develop an iPad app to measure the movement skills that support effective handwritingWith this tooltherapists will be able to identify when handwriting disability arises from a movement problemand develop treatment plans accordingly