WiRoI: Spatial Region of Interest Human Sensing with Commodity WiFi

Guo, Lingchao; Wen, Xiangming; Lu, Zhaoming; Shen, Xinbin; Han, Zijun

doi:10.1109/wcnc.2019.8886099

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…There is a similar work called WiRoI , but it used SVM classifiers instead of deep learning to recognize human movements. However, it was trained for only a single user [ 27 ]. In another similar work [ 41 ], the authors used CSI and KNN classifier to recognize five different hand gestures with an accuracy of 95% and 85% using amplitude and phase information, respectively.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…This is achieved by analyzing the received signal strength indicator (RSSI) or channel state information (CSI) of the wireless signals received by different antennas. We can use wireless signals to work in LOS or non-LOS (NLOS) environments even in dark conditions [ 12 ] and thus preserve users’ privacy [ 8 , 9 , 10 , 13 , 15 , 16 , 21 , 22 , 24 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

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Human Walking Direction Detection Using Wireless Signals, Machine and Deep Learning Algorithms

Ali,

Seytnazarov

2023

Sensors

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The use of wireless signals for device-free activity recognition and precise indoor positioning has gained significant popularity recently. By taking advantage of the characteristics of the received signals, it is possible to establish a mapping between these signals and human activities. Existing approaches for detecting human walking direction have encountered challenges in adapting to changes in the surrounding environment or different people. In this paper, we propose a new approach that uses the channel state information of received wireless signals, a Hampel filter to remove the outliers, a Discrete wavelet transform to remove the noise and extract the important features, and finally, machine and deep learning algorithms to identify the walking direction for different people and in different environments. Through experimentation, we demonstrate that our approach achieved accuracy rates of 92.9%, 95.1%, and 89% in detecting human walking directions for untrained data collected from the classroom, the meeting room, and both rooms, respectively. Our results highlight the effectiveness of our approach even for people of different genders, heights, and environments, which utilizes machine and deep learning algorithms for low-cost deployment and device-free detection of human activities in indoor environments.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Walking Direction Detection Using Wireless Signals, Machine and Deep Learning Algorithms

Ali,

Seytnazarov

2023

Sensors

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“…. , M ×Q), as PCA is widely used for helping fully extract the behavior-related features while greatly reducing the data dimensions and removing unrelated information [53]. We use the second principal component for further processing since it clearly captures human behaviors [32].…”

Section: B Data Preprocessingmentioning

confidence: 99%

Leveraging Multiple Legacy Wi-Fi Links for Human Behavior Sensing

Guo¹,

Lu²,

Wen³

et al. 2021

Preprint

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“…For example, we can implement Support Vector Machine (SVM) [60] to classify different abnormal respiration patterns: Firstly, we need to extract representative features from the detailed respiration status curves, ranging from common statistics features, such as Max, Min, Mean, Std, etc., to other features like the peak-to-peak interval vectors. Secondly, we can adopt the feature selection method used in our previous work [61] to obtain sufficient features and reduce complexity. Then using SVM, the system can distinguish different abnormal respiration patterns based on the detailed status curves with apnea periods.…”

Section: Tracking Respiration Statusmentioning

confidence: 99%

When Healthcare Meets Off-the-Shelf WiFi: A Non-Wearable and Low-Costs Approach for In-Home Monitoring

Guo,

Lu,

Zhou

et al. 2020

Preprint

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As elderly population grows, social and health care begin to face validation challenges, in-home monitoring is becoming a focus for professionals in the field. Governments urgently need to improve the quality of healthcare services at lower costs while ensuring the comfort and independence of the elderly. This work presents an in-home monitoring approach based on offthe-shelf WiFi, which is low-costs, non-wearable and makes all-round daily healthcare information available to caregivers. The proposed approach can capture fine-grained human pose figures even through a wall and track detailed respiration status simultaneously by off-the-shelf WiFi devices. Based on them, behavioral data, physiological data and the derived information (e.g., abnormal events and underlying diseases), of the elderly could be seen by caregivers directly. We design a series of signal processing methods and a neural network to capture human pose figures and extract respiration status curves from WiFi Channel State Information (CSI). Extensive experiments

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WiRoI: Spatial Region of Interest Human Sensing with Commodity WiFi

Cited by 9 publications

References 25 publications

Human Walking Direction Detection Using Wireless Signals, Machine and Deep Learning Algorithms

Human Walking Direction Detection Using Wireless Signals, Machine and Deep Learning Algorithms

Leveraging Multiple Legacy Wi-Fi Links for Human Behavior Sensing

When Healthcare Meets Off-the-Shelf WiFi: A Non-Wearable and Low-Costs Approach for In-Home Monitoring

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