CSI-Based Wireless Localization and Activity Recognition Using Support Vector Machine

Wu, Kang; Yang, Mengwei; Ma, Chuanhui; Yan, Jun

doi:10.1109/icspcc46631.2019.8960908

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…Using CSI from multiple sub-carriers, the authors of [307] built a visibility graph to capture frequency correlations between neighboring sub-carriers for SVM-based localization. next, in [308], the authors documented an SVM and kernel regression based method for localizing and recognizing activities based on CSI. In this approach, SVM performs classification of the target into an activity class while localization is accomplished by a regression model.…”

Section: B Supervised Learningmentioning

confidence: 99%

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

Trevlakis¹,

Boulogeorgos²,

Pliatsios³

et al. 2023

Preprint

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<p>When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learningbased localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.</p>

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Section: B Supervised Learningmentioning

confidence: 99%

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

Trevlakis¹,

Boulogeorgos²,

Pliatsios³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Using CSI from multiple sub-carriers, the authors of [342] built a visibility graph to capture frequency correlations between neighboring sub-carriers for SVM-based localization. next, in [343], the authors documented an SVM and kernel regression based method for localizing and recognizing activities based on CSI. In this approach, SVM performs classification of the target into an activity class while localization is accomplished by a regression model.…”

Section: ) Support Vector Machinementioning

confidence: 99%

Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an Outlook

Trevlakis,

Boulogeorgos,

Pliatsios

et al. 2023

IEEE Open J. Commun. Soc.

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When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learning-based localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.

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“…A pool of machine learning algorithms are applied to each dataset with the aim of seeing how the implementation of multiple devices can increase accuracy. The machine learning algorithms selected for this paper are Support Vector Machine (SVM) [6], K-Nearest-Neighbours (KNN) [7], Normal Linear Discriminant Analysis [8] and Random Forest [9]. 10-fold cross-validation was used on each of the three datasets.…”

Section: E Machine Learningmentioning

confidence: 99%

Contactless Healthcare Monitoring System Performance Analysis of Multiple Devices

Taylor,

Nioche,

Murray-Smith

et al. 2023

2023 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)

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Wearable sensors are often used to assist with the healthcare of elderly people within their own homes for detecting incidents such as falls. However, wearable sensors cause numerous issues: they are uncomfortable, they are often forgotten, or they simply run out of battery. One alternative to providing accurate fall detection systems is making use of ambient microwave signals to detect movements. WiFi in particular is a technology that is present in many homes and interestingly, the signal transmission is affected when humans move in between the path of transmission. However, the setup needed for using this as an effective identification method is unclear. This paper focuses here on the performance gain in using multiple devices as transmitters and receivers, instead of only one pair in a simple identification task. We report an increased accuracy in identifying a human subject sitting and standing over a pool of machine learning algorithms when the two Raspberry PI pairs of devices were used in comparison to a single pair of devices.

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CSI-Based Wireless Localization and Activity Recognition Using Support Vector Machine

Cited by 12 publications

References 15 publications

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an Outlook

Contactless Healthcare Monitoring System Performance Analysis of Multiple Devices

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