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Smart home biometric data: How domotics enable IoT data applications

When they came to market, fitness trackers and other wearables were the darlings of the IoT future, but they have since lost much of their sheen.

While the wearable market is growing according to IDC, even market-leader Fitbit faces decline in shipments, and there is little improvement in the abandonment rate — many users stop wearing their device after six months of use. The ability to wear a device is what sparked interested in the category, but has also become the barrier for IoT growth and adoption.

While wearables need to be worn to be useful, another technology can provide comparable activity data without suffering from the high abandonment rates. Interaction with devices in the home can be connected to healthcare and other business and government verticals. This collected data can be analyzed by healthcare professionals, family members, insurers, neighborhoods, cities and states for societal and homeowner benefit.

The human body has four to six clinically accepted vital signs which indicate overall condition, four of which are quantified regularly by today’s wearables: blood pressure, pulse, respiratory rate and temperature. The fifth and sixth vital signs aren’t as frequently used because they are subjective or discipline-dependent, such as pain or glucose level for diabetics. These metrics and others about bodily functions, such as sleep and menstruation, may one day be clinically considered among the others, but have limited application outside of healthcare, offering little insight for population-sized, big data analytics for IoT.

By contrast, the average smart home has many vital signs which, when quantified, produce remarkably rich data depictions — not only of human activities, but also habits, preferences, risks and changes to living patterns. This domotic data — the biometrics of the home — can be gleaned from smart home devices such as environmental sensors locks, and lights, whose metrics apply to more than just healthcare. Verticals such as security, energy, insurance, senior living, consumer products and public safety can collect smart home data, or are beginning to explore its opportunities.

There are two primary obstacles for domotic data use in IoT analytics. For one, the global smart home market is still in its growth period, with an estimated 21.8 million of U.S. households owning a smart device of some sort. As homes take on more devices, domotic data will become a de facto metric in assessing market-relevant activities and behaviors.

The second reason why domotic data isn’t being used can be attributed to the understanding of how to use it, and how it can translate into usable, scalable, IoT data. Third parties, such as businesses and government agencies, are developing uses and benefits from this data analysis, rather than by consumers themselves.

The next step for deeper IoT domotic data adoption would be a standardized lexicon, categorized into intelligent inferences scalable by time and population sizes to be customized for specific verticals. The smart home industry is still at the early stage of development, where the medium is the message. The information of door status from a smart lock has been typically understood as the data at this point, but to be useful to IoT, analysis to move beyond isolated events and device status. It needs to begin to interpret how events over the course of time become behavioral information, and then how they can be used for beneficial purposes.

Consider the ordinary smart lock, and the related devices that comprise a home security system — the most common smart home scenario. The device status of an open or closed door or window can signal departures and arrivals in the home, which is useful only to the homeowner. This simple, static information is currently only useful to residents in home monitoring and safety; but when analyzed on a larger scale, this data can play a part in larger impacts.

When examining these same conditions through industry-specific lenses, the domotic data can be used by home insurers. For example, if a family has digital codes as electronic keys for a smart lock, the assignment of additional new keys can indicate when a relative visits or a new caregiver gets access to the house. Other conditions, like habitual visitors, and other renting scenarios, such as Airbnb, can affect home security and its insurance equation.

Additional inferences can be made using the same domotic data to identify a morale hazard — a habitual inattention that incurs risk, indicated by a garage door that’s often left open. Homeowners who frequently forget to lock doors or shut off the backyard water valve during the winter fit this profile, which naturally impacts an insurance premium.

If the same data is scaled to a local region to recognize a pattern, inferences can be made about neighborhood safety, where leaving windows and garage doors open isn’t seen as risky behavior. This finding could impact insurance premiums and local policing; and at the state, above the municipal level, this data could help allocate public safety budgets more effectively. In this way, simple status notifications seen at scale have the potential to impact larger populations.

Other domotic data could yield similar external insights and commensurate responses for local and regional energy usage patterns, water consumption, residential safety risks, structural vulnerabilities in streets and buildings, senior activity and services, and more. Since its market entry more than a decade ago, Z-Wave technology has been dedicated to making these possibilities mainstream, through an interoperable, brand-agnostic approach, which creates the world’s largest ecosystem of smart home devices, functions and data generation. By providing a platform for consumers to build up their smart home with devices of their choosing, the interoperability can push smart home adoption and bring the market closer to these IoT data applications.

As the smart home market matures and both residential and commercial dwellings are outfitted with sensors, cameras and other monitoring and automation technologies, the biometrics of the home can start to make an impact.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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