Towards Device-Free Cross-Scene Gesture Recognition from Limited Samples in Integrated Sensing and Communication

Qi, Wanbin; Zhang, Ronghui; Zhou, Quan; Jing, Xiaojun

doi:10.1109/wcnc51071.2022.9771992

Cited by 3 publications

(2 citation statements)

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“…Principal Component Analysis (PCA) has been widely used for signal denoising to enhance classification performance [ 35 , 36 ]. Most of the existing works usually keep the top three principal components (especially the first component) of the data and ignore the rest, as PCA ranks the principal components in descending order in terms of their variance.The design of a first component reverse (FCR) extraction method is shown in Figure 6 .…”

Section: Methodsmentioning

confidence: 99%

MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference

Zheng,

Zhang,

Jiang

et al. 2024

Sensors

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Radio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due to low RF signal resolution and user heterogeneity. In this paper, we propose MeshID, a novel RF-based user identification scheme that enables identification through finger gestures with high accuracy. MeshID significantly improves the sensing sensitivity on RF signal interference, and hence is able to extract subtle individual biometrics through velocity distribution profiling (VDP) features from less-distinct finger motions such as drawing digits in the air. We design an efficient few-shot model retraining framework based on first component reverse module, achieving high model robustness and performance in a complex environment. We conduct comprehensive real-world experiments and the results show that MeshID achieves a user identification accuracy of 95.17% on average in three indoor environments. The results indicate that MeshID outperforms the state-of-the-art in identification performance with less cost.

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Section: Methodsmentioning

confidence: 99%

MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference

Zheng,

Zhang,

Jiang

et al. 2024

Sensors

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“…Subsequently, these features are utilized to construct higher level representations, enabling the system to recognize objects, spatial relationships, and contextual elements within the scenes. These systems have achieved the versatility of scene recognition, highlighting its significance across diverse fields such as smart home technologies ( Zhou et al, 2020 ; Qi et al, 2022 ), surveillance systems ( Sae-Ung et al, 2022 ), autonomous driving ( Arnold et al, 2019 ), healthcare systems ( Ulhaq et al, 2020 ; Angelica et al, 2021 ; Mehmood et al, 2022 ), and environmental monitoring ( Yang B. et al, 2023 ; Yang D. et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Remote intelligent perception system for multi-object detection

Alazeb,

Chughtai,

Al Mudawi

et al. 2024

Front. Neurorobot.

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IntroductionDuring the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.MethodsAdvances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.ResultsThe performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.DiscussionFurthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

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Data Collection Automation in Machine Learning Process Using Robotic Manipulator

Reczek,

Panczyk,

Wetula

et al. 2023

IFIP Advances in Information and Communication Technology

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Towards Device-Free Cross-Scene Gesture Recognition from Limited Samples in Integrated Sensing and Communication

Cited by 3 publications

References 10 publications

MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference

MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference

Remote intelligent perception system for multi-object detection

Data Collection Automation in Machine Learning Process Using Robotic Manipulator

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