On Spatial Diversity in WiFi-Based Human Activity Recognition: A Deep Learning-Based Approach

Wang, Fangxin; Gong, Wei; Liu, Jiangchuan

doi:10.1109/jiot.2018.2871445

Cited by 114 publications

(52 citation statements)

References 26 publications

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“…For example, SignFi [94] exploited the CSI signals of WiFi and was able to identify 276 sign language gestures including the head, arm, hand, and finger with CNN for classification. Wang et al [95] analyzed the impact patterns of moving humans on the WiFi signals and leveraged a combined CNN and LSTM to recognize different gestures and activities. Such method can be used for remote control of home devices such as lights and televisions [118].…”

Section: ) Smart Homementioning

confidence: 99%

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

et al. 2020

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With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field. INDEX TERMS Internet of Things, edge computing, deep learning, intelligent edge applications.

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Section: ) Smart Homementioning

confidence: 99%

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

et al. 2020

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“…A Wi-Fi signal-based spatial diversity aware non-contact activity recognition system (Wi-SDAR) was introduced. It overshadows the dead zones with only one physical Wi-Fi sender and receiver, which is fully compatible with commercial off-the-shelf Wi-Fi devices [ 53 ]. HAR uses radar as a sensor having unique characteristics such as contactless sensing and privacy protection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wi-Fi sensing using commercial hardware is widely used because it is an inexpensive and easily available solution. Human activities have been monitored and classified in existing research by ML and DL algorithms having accuracies over 90% [ 28 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. The average accuracy of promising non-contact technologies for monitoring human activities is shown in Figure 1 .…”

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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“…The research work published in [19] have presented human detection using non-linear techniques to extract CSI by examining the amounts of non-linear correlations between subcarriers. The work of [20] made use spatial diversity based on Wi-Fi to extract the CSI of human present in the dead zone. Some authors have also considered CSI based Wi-Fi signal in fall detection areas.…”

Section: Channel State Informationmentioning

confidence: 99%

Flexible and Scalable Software Defined Radio Based Testbed for Large Scale Body Movement

2020

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Human activity (HA) sensing is becoming one of the key component in future healthcare system. The prevailing detection techniques for IHA uses ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concerns as well. This paper proposes a novel, non-invasive, easily-deployable, flexible and scalable test-bed for identifying large-scale body movements based on Software Defined Radios (SDRs). Two Universal Software Radio Peripheral (USRP) models, working as SDR based transceivers, are used to extract the Channel State Information (CSI) from continuous stream of multiple frequency subcarriers. The variances of amplitude information obtained from CSI data stream are used to infer daily life activities. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis and Naïve Bayes are used to evaluate the overall performance of the test-bed. The training, validation and testing processes are performed by considering the time-domain statistical features obtained from CSI data. The K-nearest neighbour outperformed all aforementioned classifiers, providing an accuracy of 89.73%. This preliminary non-invasive work will open a new direction for design of scalable framework for future healthcare systems.

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On Spatial Diversity in WiFi-Based Human Activity Recognition: A Deep Learning-Based Approach

Cited by 114 publications

References 26 publications

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

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

Flexible and Scalable Software Defined Radio Based Testbed for Large Scale Body Movement

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