Is it possible to use a CO2 sensor to detect how many people are in a room?

Question

I have MH-Z14 Carbon Dioxide sensor and have been using it to try and detect when a room may need some fresh air. But, I've also noticed that the sensor reading drastically increases when a human is present in a room and especially if close to the sensor itself.

I am wondering if anyone tried to use the current CO2 value in a room to detect an approximate number of people in a room and how possible and accurate could it be?

Answer 1

@jsotola's comment (Something like: "Sounds like something machine learning could do") is probably the right answer, but I'll expand on it a little.

It's going to depend on at least the following factors:

• Size of the room
• Number of people
• Type of activity the people are doing
• Amount of ventilation the room has (windows/ac/...)
• Accuracy and response time of the sensor used
• Number and position of sensors

I have used data from a CO₂ sensor to roughly estimate room occupancy in the past for a single room, I didn't end up going down the machine learning route at the time, rather using things like the rate of change CO₂ to give an indicator (more people the quicker the value went up). But if I was doing it again I probably would start gathering data to use as training material.

It might also be worth fusing the data with another sensor, e.g. a relative humidity sensor as this is also likely to increase at the same time.

Answer 2

It appears some research has been done on this already – Sensing by Proxy: Occupancy Detection Based on Indoor CO₂ Concentration describes a model developed at the University of California, Berkeley to detect occupancy based on CO₂ concentration.

We propose a link model that relates the proxy measurements with unknown human emission rates based on a data-driven model which consists of a coupled Partial Differential Equation (PDE) – Ordinary Differential Equation (ODE) system.

Their model is apparently more accurate than other machine learning models they tested:

The inference of the number of occupants in the room based on CO2 measurements at the air return and air supply vents by sensing by proxy outperforms a range of machine learning algorithms, and achieves an overall mean squared error of 0.6569 (fractional person), while the best alternative by Bayes net is 1.2061 (fractional person).

Algorithm 1 (p. 3) in the paper may give some direction on how to implement a similar system to theirs, which seems to be surprisingly reliable given the simplistic nature of the CO₂ sensor.