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August 2018
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Choosing a Light or a Black Mirror

When we consider how buildings can manipulate our emotions,
we also are considering how our emotions can manipulate buildings.

Toby ConsidineToby Considine
TC9 Inc


The New Daedalus

Contributing Editor

“If it has to do with heating, lighting, or mobility for human beings on this planet, we’re interested in it.”- Darryl Willis, Google

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Last month, in the July issue, Ken Sinclair called for smart buildings to spearhead an improved relationship between the physical, the virtual and the emotional world. Relationships go two ways. When we consider how buildings can manipulate our emotions, we also are considering how our emotions can manipulate buildings.

The sci-fi anthology series Black Mirror explores a near-future where humanity's greatest innovations and dark side collide. Last week, Daikin and NEC announced that they had developed a system that monitors the movement of the employee's eyelids and hits dozing workers with a blast of cold air. More are growing aware that traditional cloud practices have a dark side in the erosion of privacy and often misuses of personal data. Will Ken’s call lead to better buildings or to their dark mirror?

Crude interactions will predominate at first because buildings have no way to empathize with their occupants. The early phases of emotional relationships with buildings will be crude, based on specific purpose driven metrics developed by building engineers responding to occupant middle management—two groups that may not be the most empathic themselves.

More subtly, Daikin / NEC collaboration begins lowering the ambient temperature when it detects people are getting sleepier.

Today, IT throws up artificial intelligence (AI) as the answer to every new problem. In the Internet of Things (IoT), this usually means combining several variants of regression analysis based on concrete models of mechanical systems. This model will not take us far down the road to Artificial Emotional Intelligence (AEI)

Humans can respond to mutual emotions because they are able to share them. In some theories, this is based on our mirror neurons. A mirror neuron fires both when we act and when we see someone else performing the same action. As we subtly shift our posture and our face to match that of those around us, we learn how they feel by feeling how we feel. Buildings can’t do this. Yet.

AEI will rely on highly abstract models of human actions and interactions. Humans are too complex to collect and transmit all data to a remote cloud, or even to the building-based cloud if there are more than a few people being tracked. Simple systems will transform data into these abstract models at the edge, and only the abstractions will be sent to the cloud. Where desirable, this enables anonymous and privatized data to be processed alongside personalized data.

These abstract models will become the “mirror neurons” of the building-based systems. Building-based systems will respond not by trying to mimic humans, but by comparing edge-based abstraction of human behavior to the abstract human models they have internally. Potential responses will then be filtered to the IoT by a repeated de-abstraction (“make alert” to “more cooling” or “more ventilation” or “more light”) to potential specific, concrete choices. The final choices will be made by traditional engineered systems, based on economic outcomes (such as energy use) and engineered choices such as ASHRAE considerations (air turns, humidity control, etc.). Edge processing will the send the abstracted effects of these choices back into the regression models.

The Classification of Everyday Life (COEL) is a recently completed specification. COEL is an OASIS specification, just as are OBIX, SAML, and the specifications for Transactive Energy. COEL is already an international ecosystem with multiple implementations based around Coelition. COEL was designed from the ground up to support modern privacy law, necessary for products to reach to international markets. COEL defines creating, transmitting, and storing the behavioral abstractions needed to create the “mirror neurons” for AEI.

This can be hard to map one’s head around. I’ll start with my own child-like understanding and description of some early COEL apps.

The hottest topics in health care are Evidence-Based Medicine and Standards of Care. Evidence-Based Medicine aims to optimize decision-making by emphasizing well-designed and well-conducted research to build strong recommendations in meta-analyses, systematic reviews, and randomized controlled trials. Standards of Care refers to detailed sequences of medicine that may continue over the years and may include, in the most difficult processes, hundreds of clinical events. A Standard of Care for orthopedic surgery may start with pre-surgery “pre-hab” (getting strong enough to benefit from the surgery), to a couple weeks of pre-surgery preparation, to the all the events the day of and the day after the surgery, to programs for rehabilitation after the surgery.

CatNet SystemsZooming in, without evidence of pre-hab fitness, it may be worthless or even dangerous to proceed to surgery. The best post-surgery outcomes involve both sending the patient home quickly and making sure the patient is returning to the level of exercise and activity. For the single patient, this requires tracking and analysis of what the patient is doing outside the hospital. For Evidence-based Medicine, this requires factoring the patient response and activity back into the meta-analyses and systematic reviews.

But what is the patient doing at home? Medical decisions during pre-hab and rehab may be based on levels of physical activity. Counting trips to the gym or physical therapist is at best inadequate and at worst misleading. One patient may go to the gym and stand around watching CNN. Another patient may not go to the gym often but might use the stairs at home and at work. Coelition member Activinsights already makes Android apps that can analyze the individual from a wearable device, abstract the data into COEL-based information, and present privacy-protecting, pseudo-anonymized, COEL Atoms to support clinical and research decisions.

While developed to support clinical work, these Apps can make personally useful predictions. Active Insights apps can predict when each person is most likely to be alert, and able to make good decisions. Simple environmental monitoring can bring a feedback loop into building operations. It is easy to imagine apps that also COEL abstractions for physical activity into personal recommendations for alertness and for re-setting the body after jet-lag.

But this is a building-based audience. It is not hard to imagine a critical meeting with attendees from many geographic locations. Personal but fully anonymized COEL biorhythm data is submitted to the building for each participant. The building then solves final schedule, ventilation, temperature, lighting level, and perhaps even lighting color to create the best chance for the best work from each participant. A conference center that can reliably do this makes a good case for a premium price. When many knowledge workers work from home or coffee shop, COEL-submissions to the scheduling server might determine the time and location of even in-town events.

It has been said that the essence of marketing is to build a relationship and engagement. Engagement can be measured as demonstrating to an individual that you know them. In smart healthcare, patient engagement is best when the patient can recognize themselves in the data. Building-based AEI enables a building to show its occupants a mirror to show them that it knows them. That mirror will be a Black Mirror unless this knowledge also protects privacy and anonymity.

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