Indoor room Occupancy Counting based on LSTM and Environmental Sensor

TL;DR

This paper presents a method for estimating classroom occupancy using CO2 sensors and LSTM deep learning models, demonstrating feasibility for real-world IoT applications.

Contribution

It introduces a novel approach combining environmental sensors and LSTM to accurately estimate occupancy in indoor environments.

Findings

The model effectively estimates classroom occupancy.

Performance evaluation shows practical applicability.

Demonstrates integration of IoT sensors with deep learning.

Abstract

This paper realizes the estimation of classroom occupancy by using the CO2 sensor and deep learning technique named Long-Short-Term Memory. As a case of connection with IoT and machine learning, I achieve the model to estimate the people number in the classroom based on the environmental data exported from the CO2 sensor, I also evaluate the performance of the model to show the feasibility to apply our module to the real environment.