New tech that senses how you're feeling inside:
is it the key to self-knowledge and the future of communication? Or
just the return of the phony "mood ring"? Check out our gallery of the
top mood-sensing technology in development.
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Computing your mood
A car that could
understand those feelings might prevent an accident, using emotional
data to flag warning signs. Sensors could nest in the steering wheel and
door handles to pick up electric signals from the skin. Meanwhile a
camera mounted on the windshield could analyze facial expressions.
Alternatively, if the
driver exhibits stress, the vehicle's coordinated sensors could soften
the light and music, or broaden the headlight beams to compensate for
loss of vision. A distressed state could be broadcast as a warning to
other motorists by changing the color of the vehicle's conductive paint.
This empathic vehicle is the goal of AutoEmotive, a research project from the Affective Computing group
at MIT's media lab, who are focused on exploring the potential of
emotional connections with machines. 'AutoEmotive' is their latest and
most integrated project, following successful efforts to make interfaces
of everything from bras to mirrors.
Researchers believe the
concept is destined for the mainstream, and have fielded interest from
manufacturers. "We have already tested most of these sensors", says
Javier Rivera, MIT researcher and project leader. "The hardware required
could easily be built into cars. Most cars have cameras anyway; you
just have more to capture the physiology. It could be done
unobtrusively."
Not time like the present
But we don't have to wait
for emotion sensors. They are flooding into a new market, using a
growing range of mood metrics to suit diverse applications. Voice
recognition app Beyond Verbal can tell you if you flirt too much in just 20 seconds. A sweater that detects skin stimulation to color code your feelings is available for pre-order.
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The fastest-developing method is facial recognition, led by Affectiva,
a start-up that spun off from MIT's Affective Computing group three
years ago. In that time, the company has amassed a database of over a
billion facial expressions, which it uses to train algorithms to
recognize and classify basic emotions such as happiness or anger, with
over 90% accuracy.
Their flagship
technology, Affdex, has been swiftly adopted by advertisers, who use it
to test reactions to their campaigns, and modify them accordingly.
Market research partners Millward Brown have standardized its use for
Fortune 500 clients including PepsiCo and Unilever.
"In the past this
technology was confined to laboratories because of high cost and slow
turnaround," explains Nick Langeveld, Chief Executive Officer of
Affectiva. "We've cracked those issues; the cost is very low as the
service is over the web, and it can be turned around almost immediately
after the data is collected."
Competitor Emotient
also specializes in face recognition, but its primary target is the
retail sector. Their software is on trial in stores, pinpointing 44
facial movements to monitor emotional reactions of staff and shoppers,
as well as demographic information including age and gender. From
customer satisfaction to employee morale, the benefits to business are
obvious, and Emotient claim major retail partners plan to make the
system permanent.
Medical applications
It is also time to bring
these tools into clinical practice, believes Dr. Erik Viirre, a San
Diego neurophysiologist. "While so many medications list suicide risk as
a possible side effect I think we have to use biosensors, and there is a
big push within psychiatry to bring them in. Thought disorders could be
picked up much quicker and used to determine treatment."
Viirre has studied
headaches extensively and found that contributing factors build up days
before they strike, including mood. He argues a multi-sensor approach
combining brain scans, genetic tests and emotion sensing could
dramatically improve treatment.
But emotion sensors are
currently limited in their capacity to differentiate nuanced expression,
says Tadas BaltruĊĦaitis of the University of Cambridge Computer
Laboratory, who has published research on the subject.
"It is easy to train a
computer to recognize basic emotions, such as fear or anger. It is more
difficult to recognize more complex emotional states, that might also be
culturally dependent, such as confusion, interest and concentration."
But there is scope for
rapid progress: "The field is relatively new, and only recently has it
been possible to recognize emotions in real world environments with a
degree of accuracy. The approaches are getting better every year,
leading to more subtle expressions being recognizable by machines."
BaltruĊĦaitis adds that
combined sensors -- as with 'AutoEmotive' -- that pick up signals from
skin, pulse, face, voice and more, could be key to progress.
Buyers beware
I think variations are already being used in places like airports and we would never know
Chris Dancy, futurist
Chris Dancy, futurist
In this post-NSA
climate, companies are keen to head off privacy concerns. Affectiva and
Emotient are vehement that all their data has been gathered with
permission from the subjects, while the latter defend their use of
recognition software in stores by saying it does not record personal
details.
But the technology is prone to abuse, according to futurist and information systems expert Chris Dancy.
"I think variations are already being used in places like airports and
we would never know", he says. "I can't imagine a system to take value
readings of my mind for a remote company being used for good. It's a
dark path."
Producers claim they
strictly control the use of their sensors, but facial recognition
technology is proliferating. UK supermarket Tesco could face legal
action for introducing it in stores without permission, while San Diego police have been quietly issued with a phone-based version.
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Ironically, Dancy -- a leading proponent of the Quantified Self movement
-- is pursuing many of the same insights into emotion as advertisers,
but by alternative means and for personal goals. He keeps himself
connected to sensors measuring pulse, REM sleep, blood sugar and more,
which he cross-references against environmental input to see how the two
correlate, using the results to give him understanding and influence
over his mind state.
'Moodhacking' has become
a popular practice among the technologically curious, and has given
rise to successful applications. Members of London's Quantified Self
Chapter created tools such as Mood Scope and Mappiness
that help the user match their mental state to external events. Hackers
and makers will have an even more powerful tool in March, when the
crowd-funded OpenBCI device makes EEG brainwaves available to anyone with a computer for a bargain price.
For all the grassroots
hostility towards corporate use of emotion sensors, there may be
convergence. Affectiva are keen to market to Quantified Self
demographics and an Affdex app for android is imminent. As the machine
learning develops, and different industries combine to join the dots, we
can all expect to be sharing a lot mo
