Most of our senses, vital signs, and actions involve the head, making the human skull one of the most interesting body locations for the simultaneous sensing and interaction of assistance applications. Although hearing aids and mobile headsets have become widely accepted as head-worn devices, users in public spaces often consider novel head-attached sensors and devices to.
In the first part of the series we explored how wearables are entering mainstream and the potential and perils of the “big” data gathered by them. This part focuses on an emerging kind of wearable computing: smart glasses and their potential.
Recently, we see a lot of wrist worn wearable devices, most dominantly smart watches and fitness trackers. However, the wrist is ergonomically a none optimal sensing position. You can get skin contact (ability to sense heart rate, skin conductivity etc.), yet, for a lot of professions it’s difficult to wear something on their wrists (doctors, maintenance workers) and also already very old studies showed that the majority of users feel obstructed by wirst-worn devices.
In contrast, the majority of our senses are situated on the head, making it one of the most interesting body placements for the sensing and interaction. Although hearing aids and mobile headsets have become widely accepted as head-worn devices, users in public spaces often consider novel head-attached sensors and devices to be uncomfortable or even condemning (see some feedback and news coverage about Google Glass as an example).
A lot of wearable computing studies provide evidence that head-worn sensing could reveal cognition-related behavior and essential vital parameters. Behavior and vital data is the key component for many cognitive assistance applications from learning aids over memory augmentation to concentration improvement. The glasses form factor seems perfect. Eyeglasses are publicly accepted accessories, often worn continuously throughout the day, rendering them an ideal platform for cognitive assistance. Subsequently, I outline our initial research towards specific cognitive assistance devices in a smart glasses form factor. So far we focused on measuring mental activities: how much you are reading and your facial expressions. Yet, the goal is to use the measures to improve user habits.
If we want to assess cognitive functions, it seems most obvious to directly observe brain activity.
On the top picture you see our progress in assessing cognitive functions in real life. From special brain sensing technology over Google Glass applications and early J!NS MEME prototypes to a more general smart glasses concept.
The more people read the larger their vocabulary and their critical thinking skills. Smart eye wear is perfect for quantifying and improving reading habits, as some people already wear some reading glasses. We already implemented a word count algorithm integrated in a smart eye wear frame. So your future glasses can tell you how much you are reading and even what type of documents. We are working on how much you understand while reading.
Next to reading and comprehension analysis, future eye wear will also be able to understand more about our emotions. To this end Masai et al. already built smart glasses that can detect facial expressions. Teh system Affective Wear detects facial expressions over photo-reflective sensors (recognizing the changes of distances between face and glass frame). Facial Expressions are a first step to understand feelings and a easy way for us to exchange information nonverbally. They can give us insights into how people think.
After gaining insights in quantifying at comprehension, cognitive load and emotions, we can continue designing interactions to improve theses mental activities. We already investigated how to improve reading immersion using nose temperature and eye movements to detect a user’s immersion and playing audio/ haptic stimuli to increase engagement. In future, we will have technology that understands and improves our cognitive functions: attention, comprehension, recall and ultimately decision making.
In this series of 3 articles I explore the impact of wearables on society more. In the next and last article, we will discuss how to get from just collecting data to actual change, from quantified self to practice design.
 Gemperle, Francine, et al. “Design for wearability.” Wearable Computers, 1998. Digest of Papers. Second International Symposium on. IEEE, 1998.
Kai Kunze works as Associate Professor at Keio Media Design. He held a position as research assistant professor at Osaka Prefecture University 2012-14.
He was a visiting researcher at the MIT Media Lab, 2011.He earned his Ph.D., summa cum laude, in the Wearable Computing field from the University of Passau in Germany, 2011.
His work experience includes research visits and internships at the Palo Alto Research Center (PARC, Palo Alto, US), Sunlabs Europe (Grenoble, France), and the German Stock Exchange (Frankfurt, Germany).