Assessing handwriting task difficulty levels through kinematic features: a deep-learning approach.

Introduction: Handwriting is a complex task that requires coordination of motor, sensory, cognitive, memory, and linguistic skills to master. The extent these processes are involved depends on the complexity of the handwriting task. Evaluating the difficulty of a handwriting task is a challenging problem since it relies on subjective judgment of experts. Methods: In this paper, we propose a machine learning approach for evaluating the difficulty level of handwriting tasks. We propose two convolutional neural network (CNN) models for single- and multilabel classification where single-label classification is based on the mean of expert evaluation while the multilabel classification predicts the distribution of experts' assessment. The models are trained with a dataset containing 117 spatio-temporal features from the stylus and hand kinematics, which are recorded for all letters of the Arabic alphabet. Results: While single- and multilabel classification models achieve decent accuracy (96% and 88% respectively) using all features, the hand kinematics features do not significantly influence the performance of the models. Discussion: The proposed models are capable of extracting meaningful features from the handwriting samples and predicting their difficulty levels accurately. The proposed approach has the potential to be used to personalize handwriting learning tools and provide automatic evaluation of the quality of handwriting.

A five generation family with a novel mutation in FOXC2 and lymphedema worsening to hydrops in the youngest generation.

We describe a five generation family with dominantly inherited lymphedema, but no distichiasis, in which 3/3 affected offspring in the fifth generation have died of fetal hydrops and related birth defects. Mutational analysis disclosed a novel mutation in FOXC2 (R121C) in affected members. We searched for possible genetic influences on the greater severity of lymphedema (hydrops) in the fifth generation. Karyotypes disclosed an extra band in Xp in one affected fetus, but this was also found in the mother. Copy number variation (CNV) studies on four members of the pedigree (mother of the three severely affected fetuses/infants; one severely affected; a full, and a half, unaffected sibs) did not detect the source of the Xp band or a possible influence on the severe phenotype. However, use of SNP arrays did allow identification of the portion of the maternal proximal Xp shared by a hydrops-affected daughter and son which was not shared by an unaffected daughter from the same sibship.