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A Machine Learning Approach to Indoor Occupancy Detection Using Non-Intrusive Environmental Sensor Data

Published: 22 August 2019 Publication History

Abstract

Over the years, Human Occupancy Measurement has had and continues to have a faire share of attention by both the research and industry communities. This long-term interest has been supported by the recent technological advances, such as the emergence of the Internet of Things (IoT), which offers a cheap alternative for gathering and processing various environmental streams of data closer to the edge, as well as machine learning techniques capable of crunching considerable amounts of raw data in real-time to produce useful and meaningful information. This paper explores and discusses the performance of a selection of machine learning algorithms applied on non-intrusive environmental sensor data (temperature and humidity) in order to infer human occupancy in closed office spaces.
This work serves as a framework to help both researchers and practitioners get a clearer idea on the efficiency and performance of each algorithm in terms of accuracy, precision, as well as other metrics. It also provides a walkthrough of time series data handling and preparation in the context of office occupancy detection. The results are also compared to a solution relying on classic data analysis methods requiring expert knowledge of the problem.

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Cited By

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  • (2024)LPM: A Lightweight Privacy-Aware Model for $\text{IoT}$ Data Fusion in Smart Connected Homes2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech61897.2024.10612646(1-7)Online publication date: 25-Jun-2024
  • (2024)Indoor Occupancy Estimation Based on Synergy of Physical Modeling, Environmental Data Fusion, and Machine Learning Frameworks2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN)10.1109/IcETRAN62308.2024.10645119(1-5)Online publication date: 3-Jun-2024
  • (2024)Smart Buildings: State-Of-The-Art Methods and Data-Driven ApplicationsIntelligent Building Fire Safety and Smart Firefighting10.1007/978-3-031-48161-1_3(43-63)Online publication date: 26-Jan-2024
  • Show More Cited By

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Published In

cover image ACM Other conferences
BDIOT '19: Proceedings of the 3rd International Conference on Big Data and Internet of Things
August 2019
139 pages
ISBN:9781450372466
DOI:10.1145/3361758
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • University of Pisa: University of Pisa
  • La Trobe University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 August 2019

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Author Tags

  1. Internet of Things
  2. Machine learning
  3. Room occupancy prediction
  4. Sensor data

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  • Research-article
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  • Refereed limited

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BDIOT 2019

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Overall Acceptance Rate 75 of 136 submissions, 55%

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Cited By

View all
  • (2024)LPM: A Lightweight Privacy-Aware Model for $\text{IoT}$ Data Fusion in Smart Connected Homes2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech61897.2024.10612646(1-7)Online publication date: 25-Jun-2024
  • (2024)Indoor Occupancy Estimation Based on Synergy of Physical Modeling, Environmental Data Fusion, and Machine Learning Frameworks2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN)10.1109/IcETRAN62308.2024.10645119(1-5)Online publication date: 3-Jun-2024
  • (2024)Smart Buildings: State-Of-The-Art Methods and Data-Driven ApplicationsIntelligent Building Fire Safety and Smart Firefighting10.1007/978-3-031-48161-1_3(43-63)Online publication date: 26-Jan-2024
  • (2023)Edge-Based Real-Time Occupancy Detection System through a Non-Intrusive Sensing SystemEnergies10.3390/en1605238816:5(2388)Online publication date: 2-Mar-2023
  • (2023)Occupancy estimation with environmental sensors: The possibilities and limitationsEnergy and Built Environment10.1016/j.enbenv.2023.09.003Online publication date: Sep-2023
  • (2022)Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor DeploymentSensors10.3390/s2210377022:10(3770)Online publication date: 16-May-2022
  • (2022)Machine Learning Techniques Towards Efficient Occupancy Detection for Industrial Urban Spaces2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON)10.1109/COM-IT-CON54601.2022.9850523(322-326)Online publication date: 26-May-2022
  • (2021)Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart EnvironmentsBig Data and Cognitive Computing10.3390/bdcc50300425:3(42)Online publication date: 8-Sep-2021
  • (2020)Estimating Occupancy Levels in Enclosed Spaces Using Environmental Variables: A Fitness Gym and Living Room as Evaluation ScenariosSensors10.3390/s2022657920:22(6579)Online publication date: 18-Nov-2020

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