Occupancy Detection and Localization by Monitoring Nonlinear Energy Flow of a Shuttered Passive Infrared Sensor

Wu, Libo; Wang, Ya; Liu, Haili

doi:10.1109/jsen.2018.2869555

Cited by 41 publications

(16 citation statements)

References 47 publications

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“…The number of movements detected by each sensor was identified in post-processing using Matlab. A total of 100 head movements were performed at a distance of 1.10 m. The installation of the PIR sensor above the pillow shows a movement accuracy detection of 85% (on average), in accordance with the technical characteristics of the used PIR sensor and the results shown in [21,22].…”

Section: Methodssupporting

confidence: 75%

Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis

Casaccia

Braccili

Scalise

et al. 2019

Sensors

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A simple sleep monitoring measurement method is presented in this paper, based on a simple, non-invasive motion sensor, the Passive InfraRed (PIR) motion sensor. The easy measurement set-up proposed is presented and its performances are compared with the ones provided by a commercial, ballistocardiographic bed sensor, used as reference tool. Testing was conducted on 25 nocturnal acquisitions with a voluntary, healthy subject, using the PIR-based proposed method and the reference sensor, simultaneously. A dedicated algorithm was developed to correlate the bed sensor outputs with the PIR signal to extract sleep-related features: sleep latency (SL), sleep interruptions (INT), and time to wake (TTW). Such sleep parameters were automatically identified by the algorithm, and then correlated to the ones computed by the reference bed sensor. The identification of these sleep parameters allowed the computation of an important, global sleep quality parameter: the sleep efficiency (SE). It was calculated for each nocturnal acquisition and then correlated to the SE values provided by the reference sensor. Results show the correlation between the SE values monitored with the PIR and the bed sensor with a robust statistic confidence of 4.7% for the measurement of SE (coverage parameter k = 2), indicating the validity of the proposed, unobstructive approach, based on a simple, small, and low-cost sensor, for the assessment of important sleep-related parameters.

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Section: Methodssupporting

confidence: 75%

Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis

Casaccia

Braccili

Scalise

et al. 2019

Sensors

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show abstract

“…Mechanical chopping [ 48 , 49 , 50 ], instead, employs a shutter to modulate the radiation received by the sensor. The shutter must be moved: a motor, therefore, is needed, thus determining a significant increase to the intrinsic sensor power consumption.…”

Section: State-of-the-art Reviewmentioning

confidence: 99%

Thermal Sensors for Contactless Temperature Measurements, Occupancy Detection, and Automatic Operation of Appliances during the COVID-19 Pandemic: A Review

2021

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The worldwide spread of COVID-19 has forced us to adapt to a new way of life made of social distancing, avoidance of physical contact and temperature checks before entering public places, in order to successfully limit the virus circulation. The role of technology has been fundamental in order to support the required changes to our lives: thermal sensors, in particular, are especially suited to address the needs arisen during the pandemic. They are, in fact, very versatile devices which allow performing contactless human body temperature measurements, presence detection and people counting, and automation of appliances and systems, thus avoiding the need to touch them. This paper reviews the theory behind thermal detectors, considering the different types of sensors proposed during the last ten years, while focusing on their possible employment for COVID-19 related applications.

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“…Yang et al [29] have carried out occupancy sensing with respect to the internet-of-things considering channel state information. Wu et al [30] have used a motion sensing approach for occupancy sensing where machine learning has been used. Study towards similar approaches of smart sensing is also carried out by Cao et al [31] with focus on intelligence building process with primary concern about energy management.…”

Section: A the Backgroundmentioning

confidence: 99%

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

K.Mane*,

Rao

2019

IJITEE

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Occupancy sensing is one of predominant technology used in various control and context aware systems. The efficiency of such systems is highly corelated with the level of accuracy of the classification model of activity or stage detection of the subject or an occupant. The classification approaches based on computer need to evolve optimally for balancing the computational and time complexity with the classication accuracy using occupancy data acquired from Doppler radar. The proposed study presents a simplified framework that emphasizes on feature extraction technique as a medium to obtain more precision in the process of monitoring occupancy. The proposed logic has been implemented using analytical methodology convention and the extracted feature has been subjected to different forms of frequently used machine learning process with respect to processing time inclusive of training and testing period, efficiency, and accuracy of the proposed system.

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Occupancy Detection and Localization by Monitoring Nonlinear Energy Flow of a Shuttered Passive Infrared Sensor

Cited by 41 publications

References 47 publications

Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis

Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis

Thermal Sensors for Contactless Temperature Measurements, Occupancy Detection, and Automatic Operation of Appliances during the COVID-19 Pandemic: A Review

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

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