Deep Learning-Based Vibration Signal Personnel Positioning System

Yu, Yang; Waltereit, Marian; Matkovic, Viktor; Hou, Weiyan; Weis, Torben

doi:10.1109/access.2020.3044497

Cited by 5 publications

(9 citation statements)

References 37 publications

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“…Through these data, they determined the patient's critical condition, which can also be used to measure the temperature of COVID-19 suspects. Similarly, the authors of [13] used piezo sensors to detect footsteps in 9 m square of floor to track the movement of persons in large production plants and buildings for the safety-relevant in emergencies. This kind of system can be used for quarantine centers.…”

Section: Iot Healthcare Systemsmentioning

confidence: 99%

“…We fine-tuned these five pre-trained models with transfer learning for training, which is much faster and easier than training a model from scratch with randomly initialized weights. In a similar way, the authors [13] used a deep neural network to analyze vibration signals that are caused by walking persons on the floor to detect the person localization in large buildings. We set the output layer of these models as non-trainable by freezing the weights and other trainable parameters in each layer to not be trained or updated when we feed our dataset.…”

Section: Mask Detection Modulementioning

confidence: 99%

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IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

Hussain

Ayoub

et al. 2021

Applied Sciences

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The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

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Section: Iot Healthcare Systemsmentioning

confidence: 99%

Section: Mask Detection Modulementioning

confidence: 99%

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

Hussain

Ayoub

et al. 2021

Applied Sciences

Self Cite

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“…The vibration signal not only contains rich environmental information but also causes no invasion of privacy. Vibration signal-based device-free situation awareness detecting approaches have attracted much attention from the academic and industry community, which shows great potential in pervasive computing applications [27][28][29].…”

Section: Related Workmentioning

confidence: 99%

“…In general, recent studies regarding vibration signal-based ubiquitous computing applications mainly use geophones (triaxial seismic sensor) [14,15,23,29,30] and piezoelectric sensors [27,28,31,32].…”

Section: Sensor Selectionmentioning

confidence: 99%

“…In addition, the piezoelectric sensor has a simpler structure, higher sensitivity, wider frequency band, and larger dynamic range [35]. However, when it is used to detect floor vibration signals caused by pedestrian walking, there are issues with poor signal quality and low signal-to-noise ratio (SNR) [28]. The characteristics and physical parameters of different piezoelectric sensors are not strictly consistent and usually present significant individual-to-individual differences.…”

Section: Sensor Selectionmentioning

confidence: 99%

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Pedestrian Counting Based on Piezoelectric Vibration Sensor

Qin

Hussain

et al. 2022

Applied Sciences

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Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods.

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A Privacy-Protecting Step-Level Walking Direction Detection Algorithm Based on Floor Vibration

Carl

Hussain

et al. 2023

IEEE Sensors J.

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Deep Learning-Based Vibration Signal Personnel Positioning System

Cited by 5 publications

References 37 publications

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

Pedestrian Counting Based on Piezoelectric Vibration Sensor

A Privacy-Protecting Step-Level Walking Direction Detection Algorithm Based on Floor Vibration

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