Fusing Sensors for Occupancy Sensing in Smart Buildings

Nasir, Nabeel; Palani, K.; Chugh, Amandeep; Prakash, Anand Krishnan; Arote, Uddhav; Krishnan, A.; Ramamritham, Krithi

doi:10.1007/978-3-319-14977-6_5

Cited by 18 publications

(9 citation statements)

References 7 publications

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“…But, instead of using SVM, we have used a Random Forest Classifier. Similarly, as in [2], we also observed that the low cost height and weight sensors provided more insight into predicting the occupant with greater accuracy when compared to the body signatures obtained from the costly device Kinect. In order to justify this we trained an ensemble model Random Forest Classifier and observed the impact due to the different features on the accuracy of prediction, as can be seen in Figure 2.…”

Section: Accuracy and Evaluationsupporting

confidence: 50%

“…There are many ways for detecting and identifying a person as described by [2]. But, instead of using SVM, we have used a Random Forest Classifier.…”

Section: Accuracy and Evaluationmentioning

confidence: 99%

“…A Smart-Door [2] consists of a door frame with multiple sensors installed on it. Two laser-phototransistor pairs of the door have been installed to detect entry or exit.…”

Section: Hardwarementioning

confidence: 99%

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Demo Abstract

Prakash

Arote

Munigala

et al. 2015

Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems

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As buildings get smarter they need to be aware of their spaces and occupants to improve prediction and management of energy consumption and environment customization based on user preference. User identification is crucial to this. However, accuracy of identification, intrusiveness and cost are important factors that one considers before installing such a system. Accounting for these factors, we built a Smart-Door that incorporates fusion of not-so-smart sensors, soft information available and learning algorithms to build an economical and accurate user identification system that requires no user intervention to monitor the occupant count and identities in a shared office space that can be scaled up to a building. It provides real-time occupancy status for the area and it can also learn to identify new users. In addition to energy management, such a user identification system has significant applications including evacuation procedures and localizing malfunctioning appliances.

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Section: Accuracy and Evaluationsupporting

confidence: 50%

“…There are many ways for detecting and identifying a person as described by [2]. But, instead of using SVM, we have used a Random Forest Classifier.…”

Section: Accuracy and Evaluationmentioning

confidence: 99%

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Demo Abstract

Prakash

Arote

Munigala

et al. 2015

Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems

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“…Artificial neural network (ANN) algorithms were used for multi-modal data fusion. In [37], a multi-sensor occupant detection system was developed with data analytics and fusion capabilities. In [38], in order to realize the recognition of human occupancy, multivariate sensors with a proposed feature extraction method and the most dominant sensor were presented and discussed.…”

Section: Introductionmentioning

confidence: 99%

“…In order to take advantage of each single detection mechanism, researchers also perform multiplesensor deployments and multi-model signal processing [34][35][36][37][38][39][40]. In [34], PIR sensors, CO 2 sensors, temperature sensors, acoustic sensors, volatile organic compounds (VOC) sensors, and infrared cameras were deployed in a test building.…”

Section: Introductionmentioning

confidence: 99%

Occupancy-Driven Energy-Efficient Buildings Using Audio Processing with Background Sound Cancellation

Huang

2018

Buildings

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Demand-driven HVAC (heating, ventilation, and air conditioning) operation is essential in occupant-oriented smart buildings, where the levels of heating, cooling, and ventilation are intelligently regulated to avoid energy waste. Despite the great potential of building energy efficiency, one of the remaining technical challenges is how to accurately estimate building occupancy information in real time. In this paper, this design challenge is addressed. An advanced audio-processing technique is adopted that minimizes the impacts of environmental sounds on the recorded voice sounds of humans. Adopted mathematical modeling and signal processing procedures are elaborated in this work. Experimental studies show that our proposed audio processing with background sound cancellation algorithm improves the estimation accuracy of room occupancy quantity by approximately 11-12%, which results in an averaged ventilation energy reduction of 3.54% compared to the case of not applying background sound cancellation. The proposed audio-processing technique is promising to achieve non-intrusive, cost-effective, robust, and accurate solutions for building occupancy estimation.

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Human-Sensing Technologies for Business Solutions

Tiwari

Kumar

et al. 2021

Lecture Notes on Data Engineering and Communications Technologies

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Fusing Sensors for Occupancy Sensing in Smart Buildings

Cited by 18 publications

References 7 publications

Demo Abstract

Demo Abstract

Occupancy-Driven Energy-Efficient Buildings Using Audio Processing with Background Sound Cancellation

Human-Sensing Technologies for Business Solutions

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