How Are You? Your Keyboard Knows.

Know-it-all.

Some months ago I took an online course — a MOOC — offered through Coursera. To identify me, Coursera’s security system asked me to type in approximately three sentences of text. Whenever the system needed to identify me again, it sampled my keystrokes. It could tell by the way I type that I was indeed the one and only G. Travis White.

That’s a pretty neat trick. The Coursera system doesn’t need to read my fingerprint or do an eye scan. It just needs to observe my typing skills. The system can easily distinguish me from all the other students in class and, conceivably, from every other human on earth. It’s simple, cheap, and hard to mimic.

So, what else can a keyboard do? Normally, when we interact with a computer, we’re transferring information. We’re asking questions and receiving answers. We’re issuing command and expecting responses. Whether we type fast or slow or hard or soft, we expect the computer to react the same way to the same input. We’re transferring information and nothing else.

But if a keyboard can uniquely identify us, could it also do more? Could it detect our emotions? And, if so, could it change the computer’s behavior based on the emotions it detects?

These are questions that several researchers at the Islamic University of Technology in Bangladesh investigated in an article recently published in Behaviour & Information Technology. As the authors point out, “Affective computing is the field that detects user emotion… [and if a machine]… can detect user emotions and change its behavior accordingly, then using machines can be more effective and friendly.”

So, how do you teach a machine to detect emotions? The researchers chose keystrokes for the same reasons that Coursera did: they’re cheap and available. The researchers also chose to combine two different methods of analysis that had previously been studied:

• Keyboard dynamics – including dwell time (how long your finger stays on a key), flight time (how long it takes to get to the next key), and content attributes (number of deletes, backspaces, etc.).

• Text pattern analysis – this usually involves identifying “affective content” by spotting specific keywords and analyzing syntax as the user chats with other users.

The researchers aimed to identify seven different emotions – anger, disgust, fear, guilt, joy, sadness, and shame as defined in the International Survey on Emotion Antecedents and Reactions (ISEAR).

And how did it work? Remarkably well. Using the two methods together produced better results than either method independently. Better yet, the results were surprisingly consistent across the range of emotions. Here’s how often the system detected an emotion correctly:

Joy                  87%

Anger             81%

Guilt               77%

Disgust           75%

Sadness          71%

Shame            69%

Fear                67%

What’s next? How about a computer that responds to your emotional state by changing its behavior in a variety of subtle and not-so-subtle ways? In other words, it becomes a true personal assistant rather than merely mechanical device. Imagine the possibilities.