# Occupancy Detection in Room Using Sensor Data

arXiv: https://arxiv.org/abs/2101.03616 · HTML: https://mlsanity.com/paper/2101.03616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 10 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Video Contents Understanding using Deep Neural Networks](https://mlsanity.com/paper/2004.13959/) (2020)
- [Stereotype-Free Classification of Fictitious Faces](https://mlsanity.com/paper/2005.02157/) (2020)
- [Gaussian Function On Response Surface Estimation](https://mlsanity.com/paper/2101.00772/) (2021)

## Built on by (most cited first, 1 of 1)

- [Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML](https://mlsanity.com/paper/2103.01992/) (2021)

## Abstract (arXiv)

With the advent of Internet of Thing (IoT), and ubiquitous data collected every moment by either portable (smart phone) or fixed (sensor) devices, it is important to gain insights and meaningful information from the sensor data in context-aware computing environments. Many researches have been implemented by scientists in different fields, to analyze such data for the purpose of security, energy efficiency, building reliability and smart environments. One study, that many researchers are interested in, is to utilize Machine Learning techniques for occupancy detection where the aforementioned sensors gather information about the environment. This paper provides a solution to detect occupancy using sensor data by using and testing several variables. Additionally we show the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms is possible to determine the occupancy of indoor environments. Seven famous algorithms in Machine Learning, namely as Decision Tree, Random Forest, Gradient Boosting Machine, Logistic Regression, Naive Bayes, Kernelized SVM and K-Nearest Neighbors are tested and compared in this study.