Indoor Occupancy Detection and Estimation using Machine Learning and Measurements from an IoT LoRa-based Monitoring System

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Abstract

In this paper, we present results on the application of machine learning to the detection of human presence and estimation of the number of occupants in our offices using data from an IoT LoRa-based indoor environment monitoring system at Aalborg University, Denmark. We cast the problem as either binary or multi-class classification and apply a two-layer feed forward neural network to the data. The data used for training, validation and testing of the network comprises of environmental data from the IoT sensors and manual recordings of the door and window states. Results show that the classifier is able to correctly determine occupancy of our offices from the IoT sensor measurements with accuracy up to 94.6% and 91.5% for the binary (presence or absence of persons) and multi-class (no person, one person or two or more persons) problems, respectively. Our analysis also shows that occupancy detection with a network trained either in another room or with single environmental parameter is also possible but with less accuracy.
Original languageEnglish
Title of host publicationGlobal IoT Summit, GIoTS 2019 - Proceedings
Number of pages5
PublisherIEEE
Publication date18 Jun 2019
Article number8766374
ISBN (Print)978-1-7281-2172-7
ISBN (Electronic)978-1-7281-2171-0
DOIs
Publication statusPublished - 18 Jun 2019
Event2019 Global IoT Summit - Aarhus, Denmark
Duration: 17 Jun 201921 Jun 2019
https://www.globaliotsummit.org/

Conference

Conference2019 Global IoT Summit
Country/TerritoryDenmark
CityAarhus
Period17/06/201921/06/2019
Internet address

Keywords

  • Machine Learning
  • Occupancy detection
  • IoT applications
  • LoRa
  • Neural networks
  • Indoor monitoring
  • Machine learning
  • Sensor data
  • IoT

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