Subcarrier selection for efficient CSI-based indoor localization

Taso, Yu; Yeh, Shih-Chun; Liang, Yu-You; Wang, Chu-Hsuan; Fang, Shih-Hau

doi:10.1088/1757-899x/383/1/012017

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…In the context of the variety of human movements and the surroundings, antennas are sensitive to external information like human movement direction and the antenna's vertical dimension. As a result, receiving antennas have different susceptibilities to different activities, for which previous works concentrated on subcarrier selection and fusion techniques [46]. However, subcarrier selection on non-sensitive antennas does not show any significance, which indicates that various antennas exhibit varying sensitivity to the same human activity.…”

Section: A Antenna Analysis For Adaptive Eliminationmentioning

confidence: 99%

Efficient Wi-Fi-Based Human Activity Recognition Using Adaptive Antenna Elimination

Jannat,

Islam,

Yang

et al. 2023

IEEE Access

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Recently, Wi-Fi-based human activity recognition using channel state information (CSI) signals has gained popularity due to its potential features, such as passive sensing and adequate privacy. The movement of various body parts in between Wi-Fi signals' propagation path generates changes in the signal reflections and refraction, which is evident from the CSI variations. In this paper, we analyzed the relationship between human activities and properties (amplitude and phase) of Wi-Fi CSI signals on multiple receiving antennas and discovered the signal properties which vary remarkably in response to human movement. The variation of the received signal among multiple antennas shows different sensitivity to human activities, directly impacting the recognition performance. Therefore, to recognize human activities with better efficiency, we proposed an adaptive antenna elimination algorithm that automatically eliminates the non-sensitive antenna and keeps the sensitive antennas following different human activities. Furthermore, the correlation of the statistical features extracted from the amplitude and phase of the selected antennas' CSI signal was analyzed, and a sequential forward selection was utilized to find the best subset of features. Using such a subset, three machine learning algorithms were employed on two available online datasets to classify various human activities. The experimental results revealed that even when using easy-toimplement, non-deep machine learning, such as random forest, the recognition system based on the proposed adaptive antenna elimination algorithm achieved a superior classification accuracy of 99.84% (line of sight) on the StanWiFi dataset and 97.65% (line-of-sight) / 93.33% (non-line-of-sight) on another widely applied multi-environmental dataset at a fraction of the time cost, demonstrating the robustness of the proposed algorithm.

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Section: A Antenna Analysis For Adaptive Eliminationmentioning

confidence: 99%

Efficient Wi-Fi-Based Human Activity Recognition Using Adaptive Antenna Elimination

Jannat,

Islam,

Yang

et al. 2023

IEEE Access

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“…Instead of using all subcarriers, dimensionality reduction techniques such as Principle Component Analysis (PCA) have been effective in concentrating artefacts of human motion from all subcarriers into a single vector [2,12,14]. Other approaches have successfully used information learning to assess the utility of different subcarriers and select the best one for subsequent feature extraction [24,25]. Tadayon et al [26] have further identified that the CSI reported by tools such as Nexmon [23] and Intel [27] do not represent the channel matrix concisely.…”

Section: Processing Csimentioning

confidence: 99%

Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

Sharma,

Li,

Mishra

et al. 2024

Energies

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Wireless-based sensing of physical environments has garnered tremendous attention recently, and its applications range from intruder detection to environmental occupancy monitoring. Wi-Fi is positioned as a particularly advantageous sensing medium, due to the ubiquity of Wi-Fi-enabled devices in a more connected world. Although Wi-Fi-based sensing using Channel State Information (CSI) has shown promise, existing sensing systems commonly configure dedicated transmitters to generate packets for sensing. These dedicated transmitters substantially increase the energy requirements of Wi-Fi sensing systems, and hence there is a need for understanding how ambient transmissions from nearby Wi-Fi devices can be leveraged instead. This paper explores the potential of Wi-Fi-based sensing using CSI derived from ambient transmissions of Wi-Fi devices. We demonstrate that CSI sensing accuracy is dependent on the underlying traffic type and the Wi-Fi transceiver architecture, and that control packets yield more robust CSI than payload packets. We also show that traffic containing upload data is more suitable for human occupancy counting, using the Probability Mass Function (PMF) of CSI. We further demonstrate that multiple spatially diverse streams of Wi-Fi CSI can be combined for sensing to an accuracy of 99%. The experimental study highlights the importance of training Wi-Fi sensing systems for multiple transmission sources to improve accuracy. This research has significant implications for the development of energy-efficient Wi-Fi sensing solutions for a range of applications.

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“…This was performed in two stages, the first is to select one subcarrier from the 64 captured ones. Subcarrier selection is used to remove redundant CSI subcarriers which can result in overfitting during machine learning processes [23]. Overfitting is when too much data is passed through the algorithm, and it causes the algorithm to memorise the training data rather than recognise patterns of the data.…”

Section: Proposed Real-time Activity Monitoring Systemmentioning

confidence: 99%

An Implementation of Real-Time Activity Sensing Using Wi-Fi: Identifying Optimal Machine-Learning Techniques for Performance Evaluation

et al. 2022

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The elderly population is growing, and the health care system is experiencing a strain on services provided to the elderly. The recent COVID-19 pandemic has increased this strain and has resulted in an increased risk of exposure during visits to elderly homes. Increasing the desire to provide technological solutions to counteract this. Currently, there lack reliable real-time non-invasive sensing systems. This paper makes use of Radio Frequency sensing, where signal propagation is observed in Channel State Information (CSI) reports on Activities of Daily Living (ADLs). Real-time data has been collected for three classifications, "movement", "empty room", and "no activity". A filter is applied to reduce the noise of the CSI data. Then the mean, max, min, kurtosis, skew and standard deviation features are extracted from the CSI data. A machine learning model provides classification for the real-time monitoring system allowing detection of abnormalities in the expected ADLs of the elderly. The timing of classifications gives insight into the real-time capabilities of the system. The Random Forest algorithm is chosen to create the machine learning model based on accuracy and timing capabilities. The model was able to achieve an accuracy of 100 % on new unseen testing data with an average classification time of 7.31 milliseconds.

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Subcarrier selection for efficient CSI-based indoor localization

Cited by 6 publications

References 13 publications

Efficient Wi-Fi-Based Human Activity Recognition Using Adaptive Antenna Elimination

Efficient Wi-Fi-Based Human Activity Recognition Using Adaptive Antenna Elimination

Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

An Implementation of Real-Time Activity Sensing Using Wi-Fi: Identifying Optimal Machine-Learning Techniques for Performance Evaluation

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