Biometric Authentication and Stationary Detection of Human Subjects by Deep Learning of Passive Infrared (PIR) Sensor Data

Andrews, Jack; Vakil, Asad; Li, J.

doi:10.1109/spmb50085.2020.9353613

Cited by 14 publications

(11 citation statements)

References 11 publications

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“…The contributions and key features of this paper are to show the usability of the proposed data preprocessing method (i.e., the Shuffle Sampling (SS) scheme, detailed in Section 3.1 ) and PIR sensor analog outputs in recognizing multi-target movement with the help of deep learning algorithms (i.e., the LSTM model in combination with the permutation invariant strategy of Section 3.4 ) and the development of an approach to explain the behavior of the ANN model. Thus, this paper extends our previous work in [ 3 ] to the PIRILS, which provides for the simultaneous localization of multiple targets by using an ANN. Specifically, the PIRILS constructs a monitor window of the multi-target locations that relates the PIR sensor’s alarm sequences to target locations.…”

Section: Introductionsupporting

confidence: 56%

“…The state-of-the-art in PIR-based localization with an artificial neural network (ANN) can be found in [ 1 , 3 , 5 , 6 ]. Yang et al [ 1 ] proposed the PIRNet architecture to localize the target, where the ANN has been composed by the target counting network and an M-person localization network.…”

Section: Related Workmentioning

confidence: 99%

“…Yun et al [ 6 ] proposed a deep learning method that combines a convolutional neural network (CNN) with the PIR sensor analog outputs to recognize the direction of human movements (e.g., capturing the analog output signals of the PIR sensors while the targets move throughout the eight directions of a monitored area). Andrews et al [ 3 ] propose a usability method of PIR sensor for stationary human presence detection and biometric authentication based on the chest motion of human subjects using a Long Short-Term Memory (LSTM) model, where the experimental result indicates that biometric authentication using PIR sensor data has a strong dependence on temporal information.…”

Section: Related Workmentioning

confidence: 99%

“…Several existing schemes using PIR-based localization show the potential of incorporating a deep learning component: modular learning in [ 1 ], a permutation-invariant approach to signal separation in [ 2 ], and target occupancy detection in [ 3 ]. Modular learning decomposes a problem into several sub-tasks and then utilizes multiple neural networks to solve them separately.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence

Chen

Wen

Sethares

2022

Sensors

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Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject’s motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target’s motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework.

show abstract

Section: Introductionsupporting

confidence: 56%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence

Chen

Wen

Sethares

2022

Sensors

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show abstract

“…At a maximum distance of one meter, a traditional analog PIR sensor, coined CM-PIR, was introduced in our previous work to solve the stationary human detection problem. CM-PIR proved 0.94 accurate at differentiating perfectly human subjects from unoccupied scenarios by detecting the movement of the chest of the monitored subject [ 41 ]. As mentioned in [ 17 , 18 ], the motion nature of MI-PIR was used to expand the normal FoV of the Fresnel lens from a manufacturer reported 93 degrees to a FoV of 223 degrees via an induced 130-degree rotation.…”

Section: Related Workmentioning

confidence: 99%

Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition

Yuan

Andrews

et al. 2022

Sensors

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Human monitoring applications in indoor environments depend on accurate human identification and activity recognition (HIAR). Single modality sensor systems have shown to be accurate for HIAR, but there are some shortcomings to these systems, such as privacy, intrusion, and costs. To combat these shortcomings for a long-term monitoring solution, an interpretable, passive, multi-modal, sensor fusion system PRF-PIR is proposed in this work. PRF-PIR is composed of one software-defined radio (SDR) device and one novel passive infrared (PIR) sensor system. A recurrent neural network (RNN) is built as the HIAR model for this proposed solution to handle the temporal dependence of passive information captured by both modalities. We validate our proposed PRF-PIR system for a potential human monitoring system through the data collection of eleven activities from twelve human subjects in an academic office environment. From our data collection, the efficacy of the sensor fusion system is proven via an accuracy of 0.9866 for human identification and an accuracy of 0.9623 for activity recognition. The results of the system are supported with explainable artificial intelligence (XAI) methodologies to serve as a validation for sensor fusion over the deployment of single sensor solutions. PRF-PIR provides a passive, non-intrusive, and highly accurate system that allows for robustness in uncertain, highly similar, and complex at-home activities performed by a variety of human subjects.

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Human Detection and Biometric Authentication with Ambient Sensors

Andrews

2022

Biomedical Sensing and Analysis

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Biometric Authentication and Stationary Detection of Human Subjects by Deep Learning of Passive Infrared (PIR) Sensor Data

Cited by 14 publications

References 11 publications

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence

Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition

Human Detection and Biometric Authentication with Ambient Sensors

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