Demo: Hands-Free Human Activity Recognition Using Millimeter-Wave Sensors

Kwon, Soo Min; Yang, Song; Liu, Jian; Yang, Xin; Saleh, Wesam; Patel, Shreya; Christine, Mathews; Chen, Yingying

doi:10.1109/dyspan.2019.8935665

Cited by 16 publications

(5 citation statements)

References 2 publications

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“…Some pipelines apply sensor fusion of millimeter wave data with data originating from other sensing domains. Sensing domains include vision [51], [61], [66], [67], [71], [95], [105], [120], [146], depth [44], [105], lidar [71], inertial measurements [89], and exerted forces [91]. Data originating from these domains complement millimeter wave data and therefore cause more accurate analytical modeling performance in several situations.…”

Section: Support From Other Sensing Domainsmentioning

confidence: 99%

“…Odometry information model performance also suffers from noise in millimeter wave data [89]. Data originating from other sensing domains have been used to guide a radar collection system to the most accurate vital sign sensing position [105] and have lead to increased analytical model performance [44], [66] and model state accuracy in object detection for visually impaired people [44]. Millimeter wave data also provide benefits to other sensing domains in return.…”

Section: Support From Other Sensing Domainsmentioning

confidence: 99%

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Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

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The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz that correspond to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology.

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Section: Support From Other Sensing Domainsmentioning

confidence: 99%

Section: Support From Other Sensing Domainsmentioning

confidence: 99%

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Some pipelines apply sensor fusion of millimeter wave data with data originating from other sensing domains. Sensing domains include vision [51], [61], [66], [67], [71], [95], [105], [120], [146], depth [44], [105], lidar [71], inertial measurements [89] and exerted forces [91]. Data originating from these domains complement millimeter wave data and therefore cause more accurate analytical modeling performance in several situations.…”

Section: Support From Other Sensing Domainsmentioning

confidence: 99%

Section: Support From Other Sensing Domainsmentioning

confidence: 99%

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Berlo¹,

Elkelany²,

Özçelebi³

et al. 2020

Preprint

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The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz corresponding to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology.

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“…Many existing studies have shown that WCS can be used in other fields, such as positioning, identification, and detection. Compared with other communication technologies, such as mmWave [3] and Bluetooth [4], the wavelength is shorter and the coverage is poor. With the proliferation of WiFi infrastructure, WiFi signals have become ubiquitous, especially in indoor scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals

Chen

Chang

2021

Sensors

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Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer learning with dynamic associate domain adaptation is proposed for human activity recognition. In order to improve the CSI quality and denoising of CSI, we carried out missing packet filling, burst noise removal, background estimation, feature extraction, feature enhancement, and data augmentation in the data pre-processing stage. This paper considers the problem of environment-independent human activity recognition, also known as domain adaptation. The pre-trained model is trained from the source domain by collecting a complete labeled dataset of all of the CSI of human activity patterns. Then, the pre-trained model is transferred to the target environment through the semi-supervised transfer learning stage. Therefore, when humans move to different target domains, a partial labeled dataset of the target domain is required for fine-tuning. In this paper, we propose a dynamic associate domain adaptation called DADA. By modifying the existing associate domain adaptation algorithm, the target domain can provide a dynamic ratio of labeled dataset/unlabeled dataset, while the existing associate domain adaptation algorithm only allows target domains with the unlabeled dataset. The advantage of DADA is that it provides a dynamic strategy to eliminate different effects on different environments. In addition, we further designed an attention-based DenseNet model, or AD, as our training network, which is modified by an existing DenseNet by adding the attention function. The solution we proposed was simplified to DADA-AD throughout the paper. The experimental results show that for domain adaptation in different domains, the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes.

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Demo: Hands-Free Human Activity Recognition Using Millimeter-Wave Sensors

Cited by 16 publications

References 2 publications

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals

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