A Survey of Deep Learning-Based Human Activity Recognition in Radar

Li, Xinyu; He, Yuan; Jing, Xiaojun

doi:10.3390/rs11091068

Cited by 236 publications

(133 citation statements)

References 100 publications

(151 reference statements)

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“…Moreover, radar technology has been successfully applied to the medical field [21,22], for example to remotely monitor the cardiac and respiratory frequency [23]. Principal Component Analysis (PCA) has been often exploited in radar applications, as an instrument to reduce the dimensionality of the available feature space and to automatize the feature extraction and selection procedure [24,25], together with deep learning algorithms for fall detection [26] and human activity recognition [27]. Recent works considered the application of deep learning techniques for gait classification, using smart sensors [28] and radar-based techniques [29] to discriminate aided from unaided motion.…”

Section: Related Workmentioning

confidence: 99%

People Walking Classification Using Automotive Radar

et al. 2020

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Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.

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Section: Related Workmentioning

confidence: 99%

People Walking Classification Using Automotive Radar

et al. 2020

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“…HAR uses radar as a sensor having unique characteristics such as contactless sensing and privacy protection. DL methods for activity recognition use radar to exploit human motion information [ 54 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Khan

Zhang

et al. 2020

Micromachines

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The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation.

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“…To avoid such drawbacks, deep learning methods were adopted to extract the appropriate motion characters automatically [15][16][17][18][19][20][21]. Convolutional neural network (CNN) is one of the most utilized deep learning structures to improve the classification accuracy for multiple human motion types [22].…”

Section: Introductionmentioning

confidence: 99%

Human Motion Serialization Recognition With Through-the-Wall Radar

Yang

Chen

Wang³

et al. 2020

IEEE Access

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Motivated by the intrinsic dynamics of physical motion as well as establishment of target motion model, this paper addresses the problem of human motion recognition with ultra wide band (UWB) through-the-wall radar (TWR) in a novel view of range profile serialization. Specifically, we first convert the original radar echoes into range profiles. Then, an auto-encoder network (AEN) with three dense layers is adopted to reduce the dimension and extract the features of each range profile. After that, a gated recurrent unit (GRU) network with two hidden layers is employed to deal with the features of each time-range slice and output the recognition results at each slice in real time. Finally, experimental data with respect to four different behind-wall human motions is collected by self-developed UWB TWR to validate the effectiveness of the proposed model. The results show that the proposed model can validly recognize the human motion serialization and achieve 93% recognition accuracy within the initial 20% duration of the activities (the average durations are 4s, 5.5s, 3s and 4.5s), which is of great significance for real-time human motion recognition. INDEX TERMS Human motion recognition, ultra wide band through-the-wall radar, auto-encoder network, gated recurrent unit.

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A Survey of Deep Learning-Based Human Activity Recognition in Radar

Cited by 236 publications

References 100 publications

People Walking Classification Using Automotive Radar

People Walking Classification Using Automotive Radar

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Human Motion Serialization Recognition With Through-the-Wall Radar

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