Indoor Human Detection Based on Thermal Array Sensor Data and Adaptive Background Estimation

Trofimova, Anna A.; Masciadri, Andrea; Veronese, Fabio; Salice, F.

doi:10.4236/jcc.2017.54002

Cited by 32 publications

(13 citation statements)

References 8 publications

(1 reference statement)

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“…Compared with the NIR sensor, the FIR sensor has a stronger anti-interference ability. Compared with other infrared sensors such as passive infrared detectors and single-point infrared sensors, infrared array sensors can provide more comprehensive information including position information, movement states, surface temperature, and others [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Tateno

Meng

Qian

et al. 2020

Sensors

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Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user’s body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application.

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Section: Methodsmentioning

confidence: 99%

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Tateno

Meng

Qian

et al. 2020

Sensors

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“…Low-resolution thermal sensors have been widely used for human detection indoors. Their advantages are low price, privacy preservation, and operation with low light [8], [7]. The Panasonic Grid-EYE has been used in several works.…”

Section: A Related Workmentioning

confidence: 99%

“…The Panasonic Grid-EYE has been used in several works. In [8], to improve accuracy by considering temperature variations from other sources than humans. In [7], to detect and track moving humans.…”

Section: A Related Workmentioning

confidence: 99%

In-Bed Person Monitoring Using Thermal Infrared Sensors

Josse¹,

Nerborg²,

Hernandez‐Diaz³

et al. 2021

Annals of Computer Science and Information Systems

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The world is expecting an aging population and shortage of healthcare professionals. This poses the problem of providing a safe and dignified life for the elderly. Technological solutions involving cameras can contribute to safety, comfort and efficient emergency responses, but they are invasive of privacy. We use 'Griddy', a prototype with a Panasonic Grid-EYE, a lowresolution infrared thermopile array sensor, which offers more privacy. Mounted over a bed, it can determine if the user is on the bed or not without human interaction. For this purpose, two datasets were captured, one (480 images) under constant conditions, and a second one (200 images) under different variations such as use of a duvet, sleeping with a pet, or increased room temperature. We test three machine learning algorithms: Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) and Neural Network (NN). With 10-fold cross validation, the highest accuracy in the main dataset is for both SVM and k-NN (99%). The results with variable data show a lower reliability under certain circumstances, highlighting the need of extra work to meet the challenge of variations in the environment.

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“…A 4x4 sensor array has been used to recognise hand motion directions [15], although the extremely low resolution of this sensor renders it unsuitable for more complex visual tasks. A 8x8 pixel sensor array has been successfully used to detect, count and track people indoors [14]. Human movements has also be inferred by using the subject's location and moving trajectory using a 16x16 sensor array [6].…”

Section: Related Workmentioning

confidence: 99%

Home Activity Monitoring using Low Resolution Infrared Sensor

Tao,

Volonakis,

Tan

et al. 2018

Preprint

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Action monitoring in a home environment provides important information for health monitoring and may serve as input into a smart home environment. Visual analysis using cameras can recognise actions in a complex scene, such as someones living room. However, although there the huge potential benefits and importance, specifically for health, cameras are not widely accepted because of privacy concerns. This paper recognises human activities using a sensor that retains privacy. The sensor is not only different by being thermal, but it is also of low resolution: 8x8 pixels. The combination of the thermal imaging, and the low spatial resolution ensures the privacy of individuals. We present an approach to recognise daily activities using this sensor based on a discrete cosine transform. We evaluate the proposed method on a state-of-the-art dataset and experimentally confirm that our approach outperforms the baseline method. We also introduce a new dataset, and evaluate the method on it. Here we show that the sensor is considered better at detecting the occurrence of falls and Activities of Daily Living. Our method achieves an overall accuracy of 87.50% across 7 activities with a fall detection sensitivity of 100% and specificity of 99.21%.

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Indoor Human Detection Based on Thermal Array Sensor Data and Adaptive Background Estimation

Cited by 32 publications

References 8 publications

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

In-Bed Person Monitoring Using Thermal Infrared Sensors

Home Activity Monitoring using Low Resolution Infrared Sensor

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