Monitoring Human Head and Neck-Based Motions From Around-Neck Creeping Wave Propagations

Bresnahan, Drew; Li, Yang; Koziol, Scott; Kim, Youngwook

doi:10.1109/lawp.2018.2839019

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…In this work, a deep convolutional neural network is trained to classify the eight driver head activities given the spectrogram images from section III as training data. Previous works have employed DCNN models for human body motion classification and have shown that well-trained models produce highly accurate and computationally efficient results for spectrogram-based image classification [5], [13], [17]. Fig.…”

Section: A Network Structurementioning

confidence: 99%

“…Previous works have investigated human head movements using a variety of sensing methods in different environments. Wearable sensors have proven viable to monitor head motion [4], [5], but this approach is too cumbersome for most natural driving applications. The two most popular noncontact approaches are camera-based video processing and radar sensing.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Driver Head Motions Using a mm-Wave FMCW Radar and Deep Convolutional Neural Network

Bresnahan

2021

IEEE Access

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Eight different driver head movements are measured using a millimeter-wave FMCW radar mounted in the dashboard of a car. The micro-Doppler signatures are converted into a spectrogram image format for analysis and classification purposes. The eight different head motions exhibit unique timefrequency profiles, which can be classified by deep learning algorithms. In this study, a convolutional neural network is used to classify the eight head motions with an optimized window size. Various dataset permutations are considered, such as the effect of window width on classification accuracy and the classification accuracy of head motions in a still car compared to a moving car.

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Section: A Network Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Driver Head Motions Using a mm-Wave FMCW Radar and Deep Convolutional Neural Network

Bresnahan

2021

IEEE Access

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“…The neck is modeled by concentric cylinders with the phantom parameters given in Table 5 and the total neck diameter is 11.5 cm. 35 The proximity effect of the neck in parallel and perpendicular orientations of the antenna is shown in Figure 10A,B, respectively. The neck's proximity affects the upper bands.…”

Section: Effect Of Human Proximity On Antennamentioning

confidence: 99%

Nearly omni‐directional compressed multiband flexible and conformal dipole antenna

Singh

Jha

Sharma

2021

Int J RF Mic Comp-Aid Eng

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A physically compressed, multiband dipole antenna on a partially optically transparent Acrylic sheet flexible substrate material is designed. The multiband operation is achieved by exploiting the higher-order resonating modes.Due to the use of the flexible substrate, and the less metallic footprint of the radiator, the antenna is flexible and conformal with the nearly omnidirectional radiation characteristic in one of the principal plane. With an S 11 = À10 dB impedance matching criterion, the antenna has a measured 0.76 to 0.85 GHz, 2.39 to 2.50 GHz, 2.83 to 2.88 GHz, and 6.09 to 6.5 GHz operating bandwidth.It also has a wide À6 dB measured impedance bandwidth from 0.74 to 0.87 GHz, 2.12 to 2.19 GHz, 2.32 to 2.52 GHz, 2.70 to 3.0 GHz, and 5.9 to 6.7 GHz. The specific absorption ratio (SAR) of the conformal antenna has been studied for the collar garment applications. The planar and conformal antennas have been fabricated and investigated for the nearly-omnidirectional radiation pattern. The simulated and measured radiation pattern shows the nearly omnidirectional pattern for a different radius of curvature. The simulated and measured results are in agreement with each other with a few exceptions. The antenna is suitable for the internet of things (IoT), body area networking (BAN), wireless fidelity (Wi-Fi), and smart city applications.

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“…There are various other evidences in literature about this method but the huge size of antenna remains a constant barrier. [3][4][5] specify such methods and their results for HAC. The above discussion gives a strong motivation for the design of miniaturized on body antennas and performance of efficient HAC using these antennas.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Classification Using On-Body Miniaturized Antennas

Peshwe

Joshi

Kothari

2022

Wireless Pers Commun

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EM waves are extremely powerful when it comes to propagation of information during communication. There is no alternative to EM waves in such applications. However, the use of EM waves or antennas in general has not been explored fully as sensors for measuring the change in physical environment. This particular aspect has been exploited and the present work focuses on the application of antennas into the domain of classification. We propose accurate human activity classification (HAC) using on body miniaturized antennas. A simple patch antenna has been designed in order to be employed on human body for HAC. The antenna has been tested with respect to its Specific Absorption Ratio (SAR) values in order to make it body-mountable. The antenna has been fabricated and tested on human body while performing daily activities. The reflection co-efficient of antennas is a function of human motion activities and this principle is exploited to achieve the desired results. To explore different approaches, a miniaturized circularly polarized antenna is then designed and tested. The results of the two antennas are then compared. Dynamic Time Warping (DTW) algorithm has been used for the analysis of the Reflection co-efficient of the antenna. Excellent activity classification performance has been obtained using both the antennas, the miniaturized antenna giving better results.

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Monitoring Human Head and Neck-Based Motions From Around-Neck Creeping Wave Propagations

Cited by 20 publications

References 17 publications

Classification of Driver Head Motions Using a mm-Wave FMCW Radar and Deep Convolutional Neural Network

Classification of Driver Head Motions Using a mm-Wave FMCW Radar and Deep Convolutional Neural Network

Nearly omni‐directional compressed multiband flexible and conformal dipole antenna

Human Activity Classification Using On-Body Miniaturized Antennas

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