In-Air Handwriting by Passive Gesture Tracking Using Commodity WiFi

Han, Zijun; Lu, Zhaoming; Wen, Xiangming; Zhao, Jingbo; Guo, Lingchao; Liu, Yue

doi:10.1109/lcomm.2020.3007982

Cited by 25 publications

(11 citation statements)

References 13 publications

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“…The tracking accuracy is also improved by 0.5 cm with the Savitzky-Golay (S-G) filter, which benefits from the smooth envelope. Besides, we compare the performance of the proposed SCE with the state-ofthe-arts, i.e., Extracting Static Component algorithm (ESC) [1] and our previous work Static Components Elimination algorithm (SCE1.0) [51]. As shown in Fig.…”

Section: Impact Of Different Moving Directionsmentioning

confidence: 99%

CentiTrack: Towards Centimeter-Level Passive Gesture Tracking with Commodity WiFi

Han¹,

Lu²,

Wen³

et al. 2020

Preprint

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Gesture awareness plays a crucial role in promoting human-computer interface. Previous works either depend on customized hardware or need a priori learning of wireless signal patterns, facing downsides in terms of the privacy concern, availability and reliability. In this paper, we propose CentiTrack, the first centimeter-level passive gesture tracking system that works with only three commodity WiFi devices, without any extra hardware modifications or wearable sensors. To this end, we first identify the Channel State Information (CSI) measurement error sources in the physical layer process, and then denoise CSI by the complex ratio between adjacent antennas. Principal Component Analysis (PCA) is further adopted to separate the reflected signals from noises. Benchmark experiments are conducted to verify that the phase changes of denoised CSI are proportional to the length changes of dynamic path reflected off the hand. In addition, we adopt the Multiple Signal Classification (MUSIC) algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then locate the initial position of hands with triangulation. We also propose a novel static componnets elimination algorithm for tracking correction by eliminating the components unrelated to motion. A prototype of CentiTrack is fully realized and evaluated in various real scenarios. Extensive experiments show that CentiTrack is superior in terms of tracking accuracy, sensing range and device cost, compared with the state-of-the-arts.

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Section: Impact Of Different Moving Directionsmentioning

confidence: 99%

CentiTrack: Towards Centimeter-Level Passive Gesture Tracking with Commodity WiFi

Han¹,

Lu²,

Wen³

et al. 2020

Preprint

Self Cite

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“…First, the camera can work only in a line-of-sight (LOS) environment with sufficient lighting [ 8 , 9 , 10 , 11 , 12 ]. Second, it does not preserve privacy concerns [ 8 , 9 , 10 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Finally, it cannot track activities or gestures through walls.…”

Section: Introductionmentioning

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%

“… In healthcare, to monitor the movement of patients in hospitals or nursing homes and alert staff if a patient is wandering or falls [ 16 , 22 , 29 ]. In smart home systems, various devices are controlled based on the location of the occupants, such as turning off lights in rooms that are not occupied or adjusting the temperature based on the presence of people in the room [ 8 , 9 , 10 , 12 , 14 , 15 , 19 , 21 , 22 , 23 , 24 , 25 , 26 , 30 ]. In retail stores, to track the movement of customers and analyze their behavior, such as the areas where they spend the most time, the products with which they interact, and the path they take through the store.…”

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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“…Another type of activity recognition is based on motion sensors. In recent years, with the rapid development of technology, the accuracy of sensors has increased, and the cost of production has been reduced [13,14]. Particularly in recent years, smartphone users have continued to increase at an almost explosive speed.…”

Section: Introductionmentioning

confidence: 99%

Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

Cao

Lin

Wang

et al. 2021

Mobile Information Systems

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The field of activity recognition has evolved relatively early and has attracted countless researchers. With the continuous development of science and technology, people’s research on human activity recognition is also deepening and becoming richer. Nowadays, whether it is medicine, education, sports, or smart home, various fields have developed a strong interest in activity recognition, and a series of research results have also been put into people’s real production and life. Nowadays, smart phones have become quite popular, and the technology is becoming more and more mature, and various sensors have emerged at the historic moment, so the related research on activity recognition based on mobile phone sensors has its necessity and possibility. This article will use an Android smartphone to collect the data of six basic behaviors of human, which are walking, running, standing, sitting, going upstairs, and going downstairs, through its acceleration sensor, and use the classic model of deep learning CNN (convolutional neural network) to fuse those multidimensional mobile data, using TensorFlow for model training and test evaluation. The generated model is finally transplanted to an Android phone to complete the mobile-end activity recognition system.

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In-Air Handwriting by Passive Gesture Tracking Using Commodity WiFi

Cited by 25 publications

References 13 publications

CentiTrack: Towards Centimeter-Level Passive Gesture Tracking with Commodity WiFi

CentiTrack: Towards Centimeter-Level Passive Gesture Tracking with Commodity WiFi

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

Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

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