Multimodal sensor fusion framework for residential building occupancy detection

A design model for building occupancy detection using sensor fusion

2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST) ◽

10.1109/dest.2012.6227924 ◽

2012 ◽

Cited By ~ 15

Author(s):

Tobore Ekwevugbe ◽

Neil Brown ◽

Denis Fan

Keyword(s):

Sensor Fusion ◽

Design Model ◽

Occupancy Detection ◽

Building Occupancy

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Multimodal Sensor Fusion for Indoor Occupancy Determination

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.764-765.1319 ◽

2015 ◽

Vol 764-765 ◽

pp. 1319-1323

Author(s):

Rong Shue Hsiao ◽

Ding Bing Lin ◽

Hsin Piao Lin ◽

Jin Wang Zhou

Keyword(s):

Bayesian Networks ◽

Sensor Fusion ◽

Dynamic Bayesian Networks ◽

Sensor Nodes ◽

Detection Accuracy ◽

Multisensor Fusion ◽

Fusion Algorithm ◽

Improve Energy Efficiency ◽

Occupancy Detection ◽

Automation Systems

Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

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Design of a Low-Level Radar and Time-of-Flight Sensor Fusion Framework

2018 21st Euromicro Conference on Digital System Design (DSD) ◽

10.1109/dsd.2018.00056 ◽

2018 ◽

Cited By ~ 2

Author(s):

Josef Steinbaeck ◽

Christian Steger ◽

Gerald Holweg ◽

Norbert Druml

Keyword(s):

Sensor Fusion ◽

Time Of Flight ◽

Low Level ◽

Fusion Framework

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AutoFuse: A Semi-supervised Autoencoder based Multi-Sensor Fusion Framework

10.1109/ijcnn52387.2021.9533389 ◽

2021 ◽

Author(s):

Kriti Kumar ◽

Saurabh Sahu ◽

Angshul Majumdar ◽

M Girish Chandra

Keyword(s):

Sensor Fusion ◽

Fusion Framework

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Investigation and analysis of Chinese residential building occupancy with large-scale questionnaire surveys

Energy and Buildings ◽

10.1016/j.enbuild.2019.04.007 ◽

2019 ◽

Vol 193 ◽

pp. 289-304 ◽

Cited By ~ 9

Author(s):

Shan Hu ◽

Da Yan ◽

Jingjing An ◽

Siyue Guo ◽

Mingyang Qian

Keyword(s):

Large Scale ◽

Residential Building ◽

Questionnaire Surveys ◽

Building Occupancy

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BLEMAT: Data Analytics and Machine Learning for Smart Building Occupancy Detection and Prediction

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213019600054 ◽

2019 ◽

Vol 28 (06) ◽

pp. 1960005 ◽

Cited By ~ 4

Author(s):

Saša Pešić ◽

Milenko Tošić ◽

Ognjen Iković ◽

Miloš Radovanović ◽

Mirjana Ivanović ◽

...

Keyword(s):

Data Analysis ◽

Experimental Validation ◽

Building Blocks ◽

Management Systems ◽

Smart Building ◽

Occupancy Detection ◽

Occupancy Patterns ◽

Building Management ◽

Building Occupancy ◽

Building Management Systems

Running costs of buildings represent a significant outlay for all businesses, thus finding a way to run facilities as efficiently as possible is vital. IoT-enabled Building Management Systems provide means for process and resource usage automation leading to overall efficiency improvements. Inferring spatial and temporal occupancy in all its forms (binary, numerical or continuous) is one of the key contextual inputs required for smart building management systems. In this work, we showcase design, implementation and experimental validation of a smart building occupancy detection and forecasting solution. The presented solution comprises three main building blocks: (1) A fog computing indoor positioning system (BLEMAT — Bluetooth Low Energy Microlocation Asset Tracking) which, combined with wireless access network monitoring processes, produces indoor location information in a semi-unsupervised manner; (2) Data analysis and pattern searching pipelines responsible for fusing data coming from different smart building and networking systems and deriving information on temporal and spatial occupancy patterns; (3) Long short-term memory (LSTM) neural networks trained to predict occupancy patterns in different areas of a smart building. Data analysis and neural network training are conducted on real-world smart building dataset which authors provide in public online repository. Experimental validation confirms that the proposed solution can provide actionable occupancy detection and prediction information, required by smart building management systems.

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