Exploring Some Handwriting Data (Experiment 1)

I have been trying to science handwriting for a year or two now, and I've had some time to dive into some recent data I collected to address some issues coming up in earlier studies. I had first run two training studies and analysed them using the lognormal model (which I blogged about here), but I immediately realised we were facing some wild individual variation; there are many ways to produce the necessary movement kinematics for a given letter and they might all be just fine. There is no single right way to produce a letter, so long as it's legible.

I therefore ran a simple study to quantify the within and between participant variation in letter production, as measured using the lognormal parameters nbLog and SNR/nbLog. A quick reminder; SNR is the signal-to-noise ratio and is a measure of the model fit; nbLog is the number of lognormal curves needed to fit the data; and the ratio of the two takes the model fit and penalises it by how hard the model had to work to get there. The data are here if you care to play. 

Participants viewed each letter of the alphabet, one at a time on a screen. Their job was to simply write that letter on a Wacom tablet where I could record the 2D kinematics of their movements. People saw each letter 10 times in a fully randomised order for a total of 260 trials.

Note: what is coming is entirely exploratory. I am literally just poking around to map out what I'm up against given the nature of the DVs. I am still figuring out the right analysis to capture what I want to say, so any thoughts welcome. 

Some Thoughts on Handwriting

A few years a go I reviewed a paper about a method, the sigma-lognormal model, to quantitatively assess handwriting (Plamondon et al, 2013). I was interested because I had in the past worked with children with developmental coordination disorder on a project developing ways to take better movement assessment out of the lab and into the clinic, and handwriting is a) something kids and their parents value and want to improve but b) is a beast to quantify. 

Réjean Plamondon kindly sent me his analysis software to play with, and I have three experiments worth of data I am currently analysing in an effort to assess whether it can help me find what I want. Here I'll briefly review the model, the experiments and some lessons I've learned training myself to write with my nondominant right hand.