Whole-home gesture recognition using wireless signals (demo)

PuQifan,; JiangSiyu,; GollakotaShyamnath,

doi:10.1145/2534169.2491687

Cited by 49 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [15], a Wi-Fi run system is built in the activity area for step estimation utilising CSI dynamics, whereas a Software Defined Radio (SDR) based non-contact system was created in [16] to recognise various human actions like standing, running, crawling, and so forth. Moreover, the authors in [17] generated a scheme based on SDR technology to hear various types of human speech, whilst [18] employed SDR technique to implement a whole house gesture recognition system.…”

Section: A Activity Monitoring Using Non-invasive Technologymentioning

confidence: 99%

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron

et al. 2021

View full text Add to dashboard Cite

Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive methods are essential especially during the COVID-19 pandemic as they minimise the burden on healthcare personnel. One notable symptom of COVID-19 infection is a rapid respiratory rate, which requires constant real-time monitoring of respiratory patterns. In this paper, Software Defined Radio (SDR) based Radio-Frequency sensing technique and supervised machine learning algorithm is employed to provide a platform for detecting and monitoring various respiratory: eupnea, biot, bradypnea, sighing, tachypnea, and kussmaul. The variations in Channel State Information (CSI) produced by human respiratory were utilised to identify distinct respiratory patterns using fine-grained OFDM signals. The proposed platform based on the SDR and the Deep Multilayer Perceptron (DMLP) classifier exhibits the ability to effectively detect and classify the aforementioned distinct respiratory with an accuracy of up to 99%. Moreover, the effectiveness of the proposed scheme in terms of diagnosis accuracy, precision, recall, F1-score, and confusion matrix is demonstrated by comparison with a state-of-the-art machine learning classifier: Random Forest.

show abstract

Section: A Activity Monitoring Using Non-invasive Technologymentioning

confidence: 99%

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The Angle of Arrival (AOA) technique is also widely used for passive tag localization. With the assistance from recent advances in the utilization of antenna arrays and Doppler Shifts, AOA based approaches can provide accurate localization accuracy [11,15]. Other works utilize fingerprint based methods to correlate the location information to some features of wireless signals at certain positions [5].…”

Section: Evaluation Of Ntrr-basedmentioning

confidence: 99%

“…ArrayTrack [23] relies on an antenna array and MIMO technique to extract the location information from the signal along the direct path (i.e., the first path) and mitigate the multipath effect. Besides locating moving persons, researchers also focus on tracking gestures without the need of wearing any device [15,24]. WiSee [15] extracts the Doppler Shift feature from wideband OFDM transmissions to recognize 9 kinds of gestures.…”

Section: Device-free Approachmentioning

confidence: 99%

“…Besides locating moving persons, researchers also focus on tracking gestures without the need of wearing any device [15,24]. WiSee [15] extracts the Doppler Shift feature from wideband OFDM transmissions to recognize 9 kinds of gestures. This research has been extended to 3D scenario; for example, the work proposed in [24] implements a prototype of 3D gesture tracking using the FMCW technology.…”

Section: Device-free Approachmentioning

confidence: 99%

See 1 more Smart Citation

Device-Free Mobile Target Tracking Using Passive Tags

Ding

et al. 2015

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

We propose a low-cost device-free mobile target tracking system using passive tags, namely, DTrack, to detect and track a human object in a certain surveillance area. The idea is that when a human object moves, he may block or reflect the RF signals between the reader and tags. Based on the estimation of Doppler Shift and Tag Read Rate variations, DTrack can detect whether a human object enters in the system and further track the moving direction of the object. DTrack uses commercial readers and off-the-shelf passive tags and is scalable and easy for large-scale deployments. Experimental results show that our system is effective in detecting the direction of moving human objects with high accuracy.

show abstract

“…The current pedestrian walking speed estimation methods can be divided into two categories: radio frequency (RF)-based methods and sensor-based methods. In RF-based walking speed estimation and activity recognition, there have been several interesting studies in recent years [4][5][6][7][8][9]. For example, Shi [7] used the fluctuation in ambient FM radio signals to infer pedestrian attention levels by interpreting changes in their walking speed and direction.…”

Section: Introductionmentioning

confidence: 99%

A Pose Awareness Solution for Estimating Pedestrian Walking Speed

Guo

Chen

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Pedestrian walking speeds (PWS) can be used as a “body speedometer” to reveal health status information of pedestrians and positioning indoors with other locating methods. This paper proposes a pose awareness solution for estimating pedestrian walking speeds using the sensors built in smartphones. The smartphone usage pose is identified by using a machine learning approach based on data from multiple sensors. The data are then coupled tightly with an adaptive step detection solution to estimate the pedestrian walking speed. Field tests were carried out to verify the advantages of the proposed algorithms compared to existing solutions. The test results demonstrated that the features extracted from the data of the smartphone built-in sensors clearly reveal the characteristics of the pose pattern, with overall accuracy of 98.85% and a kappa statistic of 98.46%. The proposed walking speed estimation solution, running in real-time on a commercial smartphone, performed well, with a mean absolute error of 0.061 m/s, under a challenging walking process combining various usage poses including texting, calling, swinging, and in-pocket modes.

show abstract

Whole-home gesture recognition using wireless signals (demo)

Cited by 49 publications

References 5 publications

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron

Device-Free Mobile Target Tracking Using Passive Tags

A Pose Awareness Solution for Estimating Pedestrian Walking Speed

Contact Info

Product

Resources

About