Wi-ESP—A tool for CSI-based Device-Free Wi-Fi Sensing (DFWS)

Atif, Muhammad; Muralidharan, Shapna; Ko, Heedong

doi:10.1093/jcde/qwaa048

Cited by 43 publications

(16 citation statements)

References 31 publications

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“…It also enables the usage of more than a single couple of transmitter/receiver to build different dataset. Two recent works have exploited ESP32 Wi-Fi CSI capabilities, Atif et al [15] proposed Wi-ESP as a tools for CSI measurement and Device-Free Wi-Fi Sensing (DFWS). The present paper evaluates the performance of ESP32 from a signal perspective and it represents a starting point useful for next research topics, although more information need to be clarified yet, such as the statement about the non-randomness of phase value collected by Wi-ESP.…”

Section: Model With Lcn Datamentioning

confidence: 99%

“…This fine-grained information was then converted to amplitude and phase mapping the value of the received raw CSI value array accordingly. More info on the OFDM modulation and the ESP32 could be found in Atif work [15].In this phase of data processing, Python code for process CSV data with Pandas library for data analysis was used. A preliminary analysis of the collected data from the ESP32is performed to identify the best strategy for post signal processing.…”

Section: Data Processingmentioning

confidence: 99%

See 1 more Smart Citation

Location intelligence system for people estimation in indoor environment during emergency operation

Fallucchi¹,

Giugliano²,

Lini³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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In the last years, location intelligence systems have been characterized by an increasing interest in several sectors. Among them, those of emergencies are mainly involved in order to enhance the rescue procedures and to reduce the intervention time, especially within indoor environment where GPS does not support the emergency operations. The authors define a low cost location intelligence system based on Channel State Information (CSI) of Wi-Fi and low-energy ESP32 SoC platform to analyze CSI data of Wi-Fi Signals. The technical solution utilizes wavelet filter to remove background noise in the CSI data, Principal component analysis (PCA) to reduce the dimensionality of the CSI data and get the most valuable data that are used as feature for the defined DNN model. The experimental results show the best performance of this model compared to the other machine learning (ML) algorithms analysed.

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Section: Model With Lcn Datamentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

Location intelligence system for people estimation in indoor environment during emergency operation

Fallucchi¹,

Giugliano²,

Lini³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…The CSI characterizes how RF signals travel from a transmitter to a receiver in an environment at different carrier frequencies and undergo various effects, such as amplitude attenuation, phase shift, and time delay [2]. The previous work has shown that the CSI-based HAR systems outperform the RSSI-based HAR systems [37].…”

Section: Related Workmentioning

confidence: 99%

Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems

et al. 2021

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A human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.

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“…This paper focuses on human behavior with the WiFi signal. Currently, WiFi signals can be measured using COTS (commercial off-the-shelf) devices, such as an Intel 5300 NIC (network interface card) [20], an Atheros 9580 [21], a Raspberry Pi [22], and an ESP32 [23]. Many studies have confirmed the feasibility of utilizing the WiFi signal to realize behavior sensing and have achieved many attractive research results.…”

Section: Introductionmentioning

confidence: 99%

A Survey of User Authentication Based on Channel State Information

Wang

Dou

et al. 2021

Wireless Communications and Mobile Computing

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Recently, human behavior sensing based on WiFi channel state information has drawn more attention in the ubiquitous computing field because it can provide accurate information about the target under a device-free scheme. This paper concentrates on user authentication applications using channel state information. We investigate state-of-the-art studies and survey their characteristics. First, we introduce the concept of channel state information and outline the fundamental principle of user authentication. These systems measure the dynamic channel state information profile and implement user authentication by exploring the channel state information variation caused by users because each user generates unique channel state information fluctuations. Second, we elaborate on signal processing approaches, including signal selection and preprocessing, feature extraction, and classification methods. Third, we thoroughly investigate the latest user authentication applications. Specifically, we analyze these applications from typical human action, including gait, activity, gesture, and stillness. Finally, we provide a comprehensive discussion of user authentication and conclude the paper by presenting some open issues, research directions, and possible solutions.

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Wi-ESP—A tool for CSI-based Device-Free Wi-Fi Sensing (DFWS)

Cited by 43 publications

References 31 publications

Location intelligence system for people estimation in indoor environment during emergency operation

Location intelligence system for people estimation in indoor environment during emergency operation

Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems

A Survey of User Authentication Based on Channel State Information

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