Wiar: A Public Dataset for Wifi-Based Activity Recognition

Guo, Linlin; Guo, Silu; Wang, Lei; Chen, Lin; Liu, Jialin; Lu, Bingxian; Fang, Jian; Liu, Zhonghao; Shan, Zeyang; Yang, Jingwen

doi:10.1109/access.2019.2947024

Cited by 69 publications

(40 citation statements)

References 53 publications

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“…When collecting the CSI data, a piece of data generally includes two pieces of blank information (i.e., it does not contain any action information, only static environment information). Such as WiAR data set, the author pointed out that each activity sample was collected with more than 7 s data which contain 2–3 s activity data (effective data) and 4–6 s empty data (indoor environment data) [ 25 ]. We use this window slicing method based on this premise.…”

Section: Methodsmentioning

confidence: 99%

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SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

Yin

Tang

2021

Sensors

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Currently, there are various works presented in the literature regarding the activity recognition based on WiFi. We observe that existing public data sets do not have enough data. In this work, we present a data augmentation method called window slicing. By slicing the original data, we get multiple samples for one raw datum. As a result, the size of the data set can be increased. On the basis of the experiments performed on a public data set and our collected data set, we observe that the proposed method assists in improving the results. It is notable that, on the public data set, the activity recognition accuracy improves from 88.13% to 97.12%. Similarly, the recognition accuracy is also improved for the data set collected in this work. Although the proposed method is simple, it effectively enhances the recognition accuracy. It is a general channel state information (CSI) data augmentation method. In addition, the proposed method demonstrates good interpretability.

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Section: Methodsmentioning

confidence: 99%

“…The RSSI represents the received power in decibels (dB), which is mathematically defined as

where V denotes the signal voltage [ 25 ]. The authors present a work that focuses on the indoor location based on RSSI of the wireless local area network (WLAN) interfaces [ 26 ].…”

Section: Preliminarymentioning

confidence: 99%

SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

Yin

Tang

2021

Sensors

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“…In this section, some state-of-art machine learning classification algorithms, which are successfully used in modern healthcare systems, are described. In Table 2 [ [126] , [127] , [128] , [129] , [130] , [131] , [132] , [133] , [134] , [135] , [136] , [137] , [138] , [139] , [140] , [141] , [142] , [143] , [144] , [145] , [146] , [147] , [148] ], existing contributions of classification algorithms utilized for healthcare are listed. Moreover, Fig.…”

Section: Machine Learning For Detection Of Covid-19 Symptomsmentioning

confidence: 99%

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed

Shah

Ahmad

et al. 2022

Journal of Pharmaceutical Analysis

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The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which caused the COVID-19 pandemic, has affected more than 250 million people worldwide. With the recent rise of a new Delta variant, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this review paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, RADAR, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

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“…A public dataset by ten volunteers with sixteen different activities in indoor environments used Wi-Fi signals to develop the Wi-AR system. The aim of the system is to reduce the cost of collected signal data for researchers in a convenient manner and improve the performance in different domains [ 44 ]. Wi-Motion uses the amplitude and phase information extracted from the CSI sequence to build the classifiers.…”

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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Wiar: A Public Dataset for Wifi-Based Activity Recognition

Cited by 69 publications

References 53 publications

SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

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

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