Creative Data > Creative Data

BEYOND THE SURFACE

KETCHUM, New York / CLOROX / 2021

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Overview

Credits

OVERVIEW

Why is this work relevant for Creative Data?

To help schools fight cold and flu outbreaks, The Clorox Company developed a new tool that is the first to passively detect and analyze coughs and sneezes in classrooms. Now in beta stage, this innovative technology enlists machine learning that can “read” spectrograms of classroom sounds, and a library of tens of thousands of coughs and sneezes curated to account for differences in language, age groups, ethnicities and voice frequencies. Then it alerts educators when a pre-determined risk threshold is reached, developed in consultation with an epidemiologist, enabling schools to pursue timely action and more strategic classroom disinfection.

Background

Inspired by the advancements in voice-driven AI (“Okay Google, play The Weeknd!”), we set out to invent a classroom tool that employed sound -- not to play music or adjust the lighting -- but to identify that something more concerning may be spreading on and beyond classroom surfaces. The tool will be the first to detect and analyze the sounds of classroom coughs and sneezes, early indicators that germs may be on the rise.

While the beta technology was in development, the COVID-19 pandemic struck. Suddenly, the technology’s potential in schools took on new meaning and urgency. So, we added thermal capability to detect fevers, enabling educators to monitor classrooms on a near real-time basis.

But that was just the beginning. Besides our mission to build a multi-symptom identification tool, we faced the daunting obstacle of monitoring a room full of students, while maintaining their privacy and anonymity.

Describe the creative idea / data solution

Clorox products have helped keep classrooms clean for generations. Unfortunately, by the time a student is ill and sees the school nurse, it’s often too late -- germs have already spread to multiple surfaces.

Educators needed an early warning tool that would give them useful data to indicate when classroom surface germs may be on the rise. So, we invented the first technology to detect and analyze the sounds of coughs and sneezes in the classroom, early signs that germs may be spreading.

Our new tool uses the power of machine learning and an audio library of over 50,000 coughs and sneezes and other classroom sounds to passively analyze a room full of students. Then, like a smoke detector, the tool proactively alerts school administrators through a custom dashboard when symptoms of an outbreak are on the uptick and additional disinfection and other measures may be needed.

Describe the data driven strategy

With our technology, educators will have access to actionable data never previously available to track the spread of germs in the classroom – near real-time cough and sneeze data that can translate into an assessment of symptoms of a potential outbreak.

While coughs or sneezes aren’t identical, they have a consistent fingerprint. Our machine learning model uses two datasets, one of cough and sneeze spectrograms (pictures of sound) and one of general classroom spectograms like laughter, chatter and moving chairs. The datasets consist of tens of thousands of spectrogram images gathered from North American households to account for variations in languages, age and voice frequencies. Additional AI thermal sensing picks up temperatures registering 100.4°F or above. Algorithms refine performance.

Placed at the front of classrooms, the tool anonymously reads coughs and sneezes and alerts educators via a custom dashboard when the data surpasses a predetermined threshold established by an epidemiologist.

Describe the creative use of data, or how the data enhanced the creative output

We collected a library of over 50,000 sounds in total to train the technology to effectively identify if the audio had the frequencies and amplitude of a cough or sneeze.

We refined our research proposal with epidemiologist Dr. Saskia Popescu and improved the prototype by adding thermal detection technology to measure students’ body temperatures.

Our technologists developed a web-based dashboard notification system to process algorithm results and view data points in real time.

Classroom data is collected anonymously, to protect individual students’ privacy.

As the beta technology neared completion, we conducted a successful field test, filmed an informational video explaining how it works and built a microsite offering the opportunity to sign up and learn more information.

Technologists continue to refine algorithms to enhance performance. Currently, research into regulatory testing/compliance is underway to determine the feasibility of bringing the technology to market.

List the data driven results

As a brand that has spent a century helping classrooms create clean, safe spaces for learning, Clorox began with a simple question, “Could we be doing more?” And “What might that look like if we harnessed the power of technology and machine learning?”

An initial field test revealed that our innovative beta technology can identify coughs and sneezes with over 95% accuracy – which earned it an enthusiastic response.

Over 1,000 people signed up on our microsite in one week, eager to learn more information as development continues.

With that, our “what if” beta tool went from theoretical test subject to a catalyst for Clorox to explore how new data-enhanced technologies can help reduce the spread of disease. We anticipate thousands of schools nationwide will be interested.

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