Paper Info
Title
A Machine Learning Approach to Indoor Occupancy Detection Using Non-Intrusive Environmental Sensor Data
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.
Year
DOI
Venue
2019
10.1145/3361758.3361775
Proceedings of the 3rd International Conference on Big Data and Internet of Things
Keywords
Field
DocType
Internet of Things, Machine learning, Room occupancy prediction, Sensor data
Environmental sensor,Time series,Data analysis,Computer science,Internet of Things,Raw data,Occupancy,Artificial intelligence,Software walkthrough,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-7246-6
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Sofiane Zemouri1182.39
Yiannis Gkoufas2175.96
John Murphy359752.43