Exploiting WiFi Channel State Information for Residential Healthcare Informatics

Tan, Bo; Chen, Qing-Chao; Chetty, Kevin; Woodbridge, K.; Li, Wenda; Piechocki, Robert J.

doi:10.1109/mcom.2018.1700064

Cited by 113 publications

(90 citation statements)

References 14 publications

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“…This system is more similar to a bistatic radar than to conventional WPS. The micro-Doppler shift caused by human activities can be further extracted from the channel state information (CSI) of the Wi-Fi, and analyzed for recognizing human actions [28], [29]. Potential applications of such techniques go far beyond the conventional indoor localization scenarios, which include health-care for elderly people, contextual awareness, anti-terrorism actions and Internet-of-Things (IoT) for smart homes [28], [30], [31].…”

Section: ) Wi-fi Based Indoor Localization and Activity Recognitionmentioning

confidence: 99%

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Liu

Masouros

Petropulu

et al. 2020

IEEE Trans. Commun.

1,150

602

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For the sake of enhancing the exploitation of the permanently allocated, but potentially under-utilized spectral resources, sharing the frequency bands between radar and communication systems has attracted substantial attention. More explicitly, there is increasing demand for sharing both the frequency band and the hardware platform between these two functionalities, but naturally, its success critically hinges on highquality joint sensing and communications. In this paper, we firstly overview the application scenarios and the research progress in the area of communication and radar spectrum sharing, with particular emphasis on: 1) Radar-communication coexistence; 2) Dual-functional radar-communication (DFRC) systems. In the remainder of the paper, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna aided user equipment (UE) operating in a mmWave channel, which in the meantime actively detects multiple targets. Note that part of the targets also play the role of scatterers for the communication signal. Given this framework, we then propose a novel scheme for joint target search and communication channel estimation relying on the omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design an HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the potential interference imposed on the UE signals by invoking the successive interference cancellation (SIC) technique, even when the radar and communication signals share the equivalent signal-to-noise ratio (SNR). The feasibility and the efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, our discussions are summarized by overviewing the open problems in the research field of CRSS.

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Section: ) Wi-fi Based Indoor Localization and Activity Recognitionmentioning

confidence: 99%

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Liu

Masouros

Petropulu

et al. 2020

IEEE Trans. Commun.

1,150

602

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“…Different human activities lead to variations in the energy and power of a signal. Power spectral density analysis (PSD) (Stoica and Moses 2005) is a common technique to analyze these effects. Thus, we compute the spectral density PSD including the frequency for the center of energy of our time series x( ) as follows.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Monitoring of human activity and fall detection systems might mitigate some of the risks. Also monitoring day to day activity would give health personal a better insight into the lifestyle of their patients and it would allow them to assist them in a more informed manner to maintain good health and ensure quick recovery (Tan et al 2018). Human activity recognition is also a key component in context aware computing, for energy efficient smart homes, fitness tracking and many internet of things (IoT) based solutions (Yousefi et al 2017;Wang et al 2015a), and in the context of disaster recovery cases (Scheurer et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Device free human activity and fall recognition using WiFi channel state information (CSI)

Damodaran

Haruni

Kokhkharova

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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Human activity recognition (HAR) is a broad research area. While there exist solutions based on sensors and vision-based technologies, these solutions suffer from considerable limitations. Thus in order to mitigate or avoid these limitations, device free solutions based on radio signals like (home) WiFi, in particular 802.11 are considered. Recently, channel state information (CSI), available in WiFi 802.11n networks have been proposed for fine-grained analysis. We are able to detect human activities like Walk, Sit, Stand, Run (in the sequel, any human activity used for classification is capitalised, i.e. is denoted by its corresponding label. For example, "standing" is denoted as Stand, the activity "sitting" is denoted by Sit and so on), etc. in a line-of-sight (LOS) scenario and a non-line-of-sight (N-LOS) scenario within an indoor environment. We propose two algorithms-one using a support vector machine (SVM) for classification and another one using a long shortterm memory (LSTM) recurrent neural network. While the former uses sophisticated pre-processing and feature extraction techniques based on wavelet analysis, the latter processes the raw data directly (after denoising). We show that it is possible to characterize activities and/or human body presence with high accuracy and we compare both approaches with regard to accuracy and performance. Furthermore, we extend the experimental setup to detect human falls, too which is a relevant use-case in the context of ambient assisted living (AAL) and show that with the developed algorithms it is possible to detect falls with high accuracy. In addition, we also show that the algorithms can be used to count the number of people in a room based on the CSI-data, which is a first step towards detecting more complex social behavior and activities. Our paper is an extended version of the paper (Damodaran and Schäfer, Device free human activity recognition using wifi channel state

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“…There, the actual signal is obfuscated in noise, brought on by shadowing effects and fading. Recently, there have been some work done using Channel State Information (CSI) [147,175]. Using newer standards, such as IEEE 802.11, one can extract the amplitude and phase information from the channel directly, offering better performance [147].…”

Section: Radio Frequency Sensorsmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

Self Cite

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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Exploiting WiFi Channel State Information for Residential Healthcare Informatics

Cited by 113 publications

References 14 publications

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Device free human activity and fall recognition using WiFi channel state information (CSI)

Sensor Modalities and Fusion for Robust Indoor Localisation

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