Monitoring small children
There are electronic products (e.g., Itsbeen, basically a stopwatch on steroids) that help new parents keep track of when the baby last slept, ate, poo’ed, etc. They do not, however, capture that data for analysis. My wife and I would like to see the historical data to see if we can tease out some insights about our son (e.g., how much sleep does he need before he gets cranky?).
Initial approaches did not work so well …
We tried using an iPhone app called Blogger that helps parents keep track of these things, but it wasn’t immediate enough. We ended-up writing down events on the nursery mirror with a dry erase pen, but I really wanted to track things via a single button press. By the time I’ve finished dodging multiple salvos of pee and poo, multiple diaper changes due to said peeing and pooing, spit-up, puking, and sundry other lovely activities (a testament to how much I love you, boy), I can’t remember anything that’s happened in the last five minutes, let alone the last hour or two.
I certainly remember that feeling …
On the topic of monitoring and small children, there was a recent report in MIT Technology Review (and other places) about a device called LENA that tries to detect autism by analyzing speech patterns in a child who is wearing it. The device is put into the pocket of a specially designed piece of clothing, after which it records the sonic environment of the child (including the child’s own speech, of course). At the end of the day, the device is plugged into a computer to analyze the recordings. This procedure is repeated 2 to 3 times a month.
LENA is meant for children between 0 and 4 years. According to the Tech Review article, the average age for autism diagnosis is 5.7 years, but it would be preferable if it was detected much earlier as intervention is more effective when the child is two to four years old. The article explains:
“Roughly speaking, autistic children vocalize differently from other children,” explainsDongxin Xu,manager of software and language engineering at the LENA Foundation. While this concept isn’t new, clinicians have had a hard time using it to their best advantage when making a diagnosis. The problem is, in part, logistical: with existing methods, it is hard to collect enough good-quality data.
Even if enough data is gathered, analyzing the audio is still extremely difficult and time-consuming. Making a diagnosis isn’t as simple as counting the number of times the child makes a certain kind of sound, explains Jeffrey Richards, a statistician and database technician for the LENA Foundation.
Richards says the LENABaby software, which he helped develop, starts by breaking down the 16-hour audio stream into segments. Each segment is automatically classified according to the type of sound contained in the clip, such as sounds from the child, a parent, or television. Vocalizations from the child are then assessed further using complex algorithms that look at a variety of factors, such as the phonological composition of the each sound and how sounds are clustered and paired.