But the data collected from the wifi receiver doesn’t necessarily tell the whole story. If five people in a room of 10 are all fidgeting at the same time, the data simply shows a period of movement which the researchers call a “Crowd Fidgeting Period” (periods of non-movement are instead called a “Crowd Silent Period”) that has no real information about how many people are actually moving. To create a headcount, the researchers had to develop a new mathematical model, but were actually able to borrow some tools from another field of mathematics that deals with something known as Queuing Theory: the study of waiting lines and queues that can be used to predict wait times.
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More details about the mathematical model the team developed can be found in the results of their study which were recently published. After testing the new wifi counting technique in 47 experiments conducted in four different environments with groups of 10 people or less, the accuracy was found to be very high: 96.3% of the time, the estimated number of people seated in a space was either exact or off by one, with accuracy dropping to 90% when the wifi signals were also passing through walls.
As with the previous research, being able to accurately count the number of people in a space has some very useful applications, and not just for quickly figuring out how many people attended a TED talk, or stuck around until the end of a movie. For consumers it could be used to easily augment the intelligence of a heating and cooling system, allowing it to increase or decrease temperatures based on the number of people in a room. During an ongoing pandemic, it could also be used as tool for keeping tabs on how many people are in an establishment, such as a restaurant, in cities where seating capacities are limited to help curb the spread of the virus, automating an otherwise time-consuming task.
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