Blog
Accurate Data Labeling Makes Your Smart Homes Smarter

Accurate Data Labeling Makes Your Smart Homes Smarter

Ever wondered how smart homes earn that title when some AI-enhanced security systems identify your neighbor’s cat as a security threat? It’s either because you live next door to the zoo — or it’s the result of inferior computer vision data labeling.

What are smart homes and smart appliances?

Smart homes empower homeowners to remotely control and program various systems and appliances, either from within their homes or via the internet.

The list of “smart appliances” is constantly growing and includes everything from TVs, lights, and laundry machines to fridges, ovens, security systems, and beyond. These appliances are interconnected and exchange data via central systems — otherwise known as the Internet of Things — and can be programmed to respond to sensors detecting input such as movement, light levels, or temperature. 

Data Labeling Smart Homes

While the smart home might seem like a product of the internet era, the buzz around them began as far back as the 1930s when local fairs featured automatized home appliances. The American Association of Home Builders coined the term “smart home” in 1984, yet the first connected home appliance — a toaster — didn’t hit the market until 1990.

Fast forward to today, and around 43% of US households report owning at least one smart home device, up 30% from 2019. Consumers have more options than ever to interact with appliances from every room in their house, from their laundry rooms and kitchens to their entertainment centers. 

Let’s take a walk through a modern smart home to see some of the latest smart appliance trends — and the data labeling challenges that come with them.

Loads of help in the laundry room

If doing the laundry is your least favorite chore, companies such as Whirlpool are manufacturing devices to iron out the process. Their smart devices enable you to operate your machines remotely — even from, say, the grocery store — so you can kill two chores with one phone.

And for those times you’ve got your hands full (literally), smart device compatibility enables you to ask your Alexa or Google device to start the load for you. Some models even contain sensors that can identify fabric type, load size, and soil level — and automatically adjust settings accordingly. 

Smart kitchen appliances help you save thyme

Advances in computer vision and machine learning have enabled companies like Samsung and GE to patent and manufacture smart ovens and refrigerators. These appliances enable homeowners to see inside their fridges from their smartphones to help with grocery shopping and meal planning.

The technology will evolve to include features to identify food spoilage and automated, permission-based food ordering. Some futurists even predict that smart toilets will someday make dietary recommendations based on your fecal matter… 

Home security systems keep an eye on your assets

Thanks to accessible home security systems and video doorbells, anyone can easily differentiate between a burglar and their son getting home from school.

Many home comfort, security, and lighting systems have been enhanced with facial recognition technology, motion sensors, and fingerprint scanners for locking doors when your family members don’t have a key.

Some predict that in the near future, personal drones will continuously hover around your house for exterior home security.

Smart appliance data labeling challenges

While the smart homes of the future sure paint a rosy picture of a leisurely and safe weekend at home, none of this is possible without high-quality labeled data.

Manufacturers must ensure their products deliver accurate insights to compel consumers to invest in smart home appliances and systems. In other words, your smart fridge needs to be more performant than checking yourself if you’re out of eggs.

For smart home appliances that leverage computer vision to “see” the world, many of the same data labeling challenges can arise:

    • Accurate CV models require sizeable and diverse training datasets.
      Find an annotation partner who can scale to meet demand without compromising on quality, and help you identify which data you should have labeled first.
    • In production, your smart appliance may encounter a variety of different lighting conditions, angles, and occlusions.
      Rain shouldn’t compromise the performance of your CV-powered security system. Tight feedback mechanisms should be in place in your labeling process to capture and resolve edge cases as they arise.
    • Smart devices — and the wealth of data they create — can be vulnerable to data privacy and security breaches.
      Partner with a data training provider who understands the importance of security in your industry, and proactively implements physical and digital security measures to protect your data.

The insights that smart home appliances of the future will need to deliver are more than the human eye can capture. High-quality labeled training data is essential to the future of computer vision-enhanced intelligent appliances.

Want to learn more about working with Sama on training smart home devices and surveillance systems?

Related Resources

In-House vs Outsourcing Data Annotation for ML: Pros & Cons

13 Min Read

Sama’s Experiment-Driven Approach to Solving for High-Quality Labels at Scale

6 Min Read

ML Assisted Annotation Powered by MICROMODEL Technology

8 Min Read