Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data

Arvidsson, Simon; Gullstrand, Marcus; Sırmaçek, Beril; Riveiro, Maria

doi:10.3390/s21041036

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…A large amount of recent work on occupancy detection has focused on sensor fusion techniques [54][55][56][57]. These systems generally utilize a variety of low-cost and low-intrusion sensors, such as CO 2 , relative humidity, and ambient noise.…”

Section: Other Detection Methodsmentioning

confidence: 99%

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Jacoby

Tan

Katanbaf

et al. 2021

JSAN

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Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

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Section: Other Detection Methodsmentioning

confidence: 99%

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Jacoby

Tan

Katanbaf

et al. 2021

JSAN

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“…The use of TSA in fall detection has emerged in recent years to bridge the gap between performance and user privacy concerns [10], [24], [25], [26]. TSA has also been proposed in other applications including human distance estimation [27], physical distancing [28], [29], occupancy estimation [14], [30], [31], [32], and human activity recognition [33], [34]. A critical comparison of TSA settings, data-driven methods, and performance of the state-of-art TSA-based fall detection systems has been provided in Tables I and II.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving, Thermal Vision With Human in the Loop Fall Detection Alert System

Naser

Lotfi

Mwanje

et al. 2023

IEEE Trans. Human-Mach. Syst.

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To support the independent living of older adults in their own homes, it is essential to identify their abnormal behaviors before triggering an automated alert system. Existing normal vision sensing approaches to detect human falls in the activities of daily living (ADL) experienced acceptability issues due to outstanding privacy concerns when they are deployed in personal environments. Besides, false alerts (false-positive) fall detection has not been addressed thoroughly in systems that report abnormal human behaviors as emergency alerts to the information support. This article proposes a novel human-in-the-loop fall detection approach in the ADLs using a low-resolution thermal sensor array. The motivation for enabling a human interactive model, fall detection confirmation, is to influence resource efficiency by reducing false-positive alerts while keeping the false-negative fall predictions as low as possible. The proposed approach is based on the motion sequence classification of human movements using a recurrent neural network. The proposed approach is evaluated with comprehensive experiments using different learning techniques, users, and domestic environment conditions. This article shows a performance accuracy of 99.7% to detect human falls from various typical ADLs.

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“…Deep learning uses graph technologies and neuron transformations to obtain multilayer learning models and automatically learns the data. The most widely used deep learning models are Convolutional Neural Network (CNN) [119] and Recurrent Neural Networks (RNN) [120], which are also popular in building occupancy prediction. Also, the development of deep learning algorithms provides advancement in building automation systems as it can convert the data at one level (starting with the natural data) into a depiction at a slightly more abstract level.…”

Section: The Trends Of Machine Learning and Deep Learningmentioning

confidence: 99%