Multimodal sensor fusion framework for residential building occupancy detection

Tan, Sin Yong; Jacoby, Margarite; Saha, Homagni; Florita, Anthony; Henze, Gregor P.; Sarkar, Soumik

doi:10.1016/j.enbuild.2021.111828

Cited by 18 publications

(6 citation statements)

References 27 publications

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“…The recent research in model-based systems includes an Occupancy detection framework. The work combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using the ensemble method (Tan et al, 2022a).…”

Section: Predictive Models Based On Machine Learningmentioning

confidence: 99%

A state-of-the-art artificial intelligent techniques in daylighting controller: models and performance

Grace Colaco,

Varghese,

Pearl Kurian

et al. 2023

STET

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Lighting designers are always on the quest to develop a lighting control strategy that is aesthetically pleasing, comfortable, and energy-efficient. In an indoor context, electric lighting blended with daylighting controls forms a quintessential component for improving the occupant’s comfort and energy efficiency. Application of soft computing techniques, adaptive predictive control theory, machine learning, HDR photography, and wireless networking have facilitated recent advances in intelligent building automation systems. The evolution and revolution from the 19th to the 21st century in developing daylighting control schemes and their outcomes are investigated. This review summarizes the state-of-the-art artificial intelligence techniques in daylighting controllers to optimize the performance of conventional photosensor-based control and camera-based control in commercial buildings. The past, current, and future trends are investigated and analyzed to determine the key factors influencing the controller design. This article intends to serve as a comprehensive literature review that would aid in creating promising new concepts in daylighting controllers.

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Section: Predictive Models Based On Machine Learningmentioning

confidence: 99%

A state-of-the-art artificial intelligent techniques in daylighting controller: models and performance

Grace Colaco,

Varghese,

Pearl Kurian

et al. 2023

STET

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“…According to the review, the majority of works employ Convolutional Neural Networks and Multilayer Perceptron Algorithms. Cameras, microphones, and environmental sensors were used in [6] for indoor occupancy detection. The authors trained individual models for each data modality and combined them using an ensemble approach.…”

Section: State Of the Artmentioning

confidence: 99%

“…Combining (3) and ( 5), a final score for each class is formed. The final predicted label for each test case is the class with the maximum final score (6).…”

Section: First Experimental Setupmentioning

confidence: 99%

“…The optimization method aims to find the best set of weights that maximize a desired metric on the train set. The desired metric is computed via the final score (6). The set of weights that produces the optimal results is then used with predicted probabilities from all the sensors in the test set, as in (6), to make the final prediction.…”

Section: First Experimental Setupmentioning

confidence: 99%

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Fusion of Environmental Sensors for Occupancy Detection in a Real Construction Site

Tsanousa,

Moschou,

Bektsis

et al. 2023

Sensors

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Internet-of-Things systems are increasingly being installed in buildings to transform them into smart ones and to assist in the transition to a greener future. A common feature of smart buildings, whether commercial or residential, is environmental sensing that provides information about temperature, dust, and the general air quality of indoor spaces, assisting in achieving energy efficiency. Environmental sensors though, especially when combined, can also be used to detect occupancy in a space and to increase security and safety. The most popular methods for the combination of environmental sensor measurements are concatenation and neural networks that can conduct fusion in different levels. This work presents an evaluation of the performance of multiple late fusion methods in detecting occupancy from environmental sensors installed in a building during its construction and provides a comparison of the late fusion approaches with early fusion followed by ensemble classifiers. A novel weighted fusion method, suitable for imbalanced samples, is also tested. The data collected from the environmental sensors are provided as a public dataset.

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“…Tekler et al [24] combined CO 2 , Wi-Fi connected devices, and many other sensor data Buildings 2023, 13, 2832 2 of 18 types to predict occupancy. Tan et al [25] combined temperature, humidity, image, and many other sensor data types to detect occupancy. Qaisar et al [26] combined temperature, humidity, HVAC operations, and many other sensor data types to predict occupancy.…”

Section: Introductionmentioning

confidence: 99%

A Cost-Effective System for Indoor Three-Dimensional Occupant Positioning and Trajectory Reconstruction

Zhao,

Li,

Zhao

et al. 2023

Buildings

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Accurate indoor occupancy information extraction plays a crucial role in building energy conservation. Vision-based methods are popularly used for occupancy information extraction because of their high accuracy. However, previous vision-based methods either only provide 2D occupancy information or require expensive equipment. In this paper, we propose a cost-effective indoor occupancy information extraction system that estimates occupant positions and trajectories in 3D using a single RGB camera. The proposed system provides an inverse proportional model to estimate the distance between a human head and the camera according to pixel-heights of human heads, eliminating the dependence on expensive depth sensors. The 3D position coordinates of human heads are calculated based on the above model. The proposed system also associates the 3D position coordinates of human heads with human tracking results by assigning the 3D coordinates of human heads to the corresponding human IDs from a tracking module, obtaining the 3D trajectory of each person. Experimental results demonstrate that the proposed system successfully calculates accurate 3D positions and trajectories of indoor occupants with only one surveillance camera. In conclusion, the proposed system is a low-cost and high-accuracy indoor occupancy information extraction system that has high potential in reducing building energy consumption.

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Multimodal sensor fusion framework for residential building occupancy detection

Cited by 18 publications

References 27 publications

A state-of-the-art artificial intelligent techniques in daylighting controller: models and performance

A state-of-the-art artificial intelligent techniques in daylighting controller: models and performance

Fusion of Environmental Sensors for Occupancy Detection in a Real Construction Site

A Cost-Effective System for Indoor Three-Dimensional Occupant Positioning and Trajectory Reconstruction

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