WiTrace: Centimeter-Level Passive Gesture Tracking Using OFDM Signals

Wang, Lei; Sun, Ke; Dai, Haipeng; Wang, Wei; Huang, Kang; Liu, Alex X.; Wang, Xiaoyu; Gu, Qing

doi:10.1109/tmc.2019.2961885

Cited by 20 publications

(5 citation statements)

References 29 publications

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“…In recent years, there are increasing work on CSI-based contact-free tracking. However, most of them are focusing on single-target tracking (e.g., one walking person, a waving limb or finger) [4]- [6], [22]. The common idea behind these pioneer works is to extract the location-or velocityrelated parameters from the CSI.…”

Section: B Practical Challengesmentioning

confidence: 99%

Contact-Free Multitarget Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis

Bast

Miao

et al. 2023

IEEE Internet Things J.

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Wireless-based human activity recognition has become an essential technology that enables contact-free humanmachine and human-environment interactions. In this paper, we consider contact-free multi-target tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6 GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets' locations based on the extracted target-ofinterest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density (GM-PHD) filter for tracking. A multi-pedestrian tracking experiment is conducted in a room with a size of 6.5 m×10 m to evaluate the performance of the proposed algorithm. According to experimental results, we achieve 75th and 95th percentile accuracy of 12.7 cm and 18.2 cm for single-person tracking and 28.9 cm and 45.7 cm for multi-person tracking, respectively. Furthermore, the proposed algorithm achieves tracking purposes in real-time, which is promising for practical MTT use cases.

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Section: B Practical Challengesmentioning

confidence: 99%

Contact-Free Multitarget Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis

Bast

Miao

et al. 2023

IEEE Internet Things J.

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“…Introduction: With the popularity of intelligent devices, humancomputer interaction is no longer limited to mice, keyboards, and touch screens. In recent years, the gesture recognition based on channel state information (CSI) of WiFi has drawn considerable research attention, such as WiTrace [1], WiHGR [2] and WiPass [3]. Compared with vision-based and wearable sensor-based sensing schemes, the CSI-based scheme has the advantages of low cost, easy deployment, non-light of sight sensing, and device-free.…”

Section: Cross-domain Extendable Gesture Recognition System Using Wif...mentioning

confidence: 99%

Cross-domain extendable gesture recognition system using WiFi signals

Yu-xi

2023

Preprint

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This letter proposes a cross-domain WiFi-based gesture recognition system (WiCross) based on a dynamically weighted multi-label generative adversarial network. Most existing WiFi-based gesture recognition systems are user, orientation, and environment sensitive, which limits the application of WiFi sensing. Compared with the influence of users and environments on WiFi sensing systems, the influence of orientation on WiFi sensing systems is more difficult to remove. To alleviate the confusion caused by the orientation more effectively, we arrange the transmitting and receiving antennas according to the characteristics of the Fresnel region. We propose to dynamically weight different links according to users’ orientations and use a multi-label generative adversarial network to obtain domain-independent features. More importantly, WiCross can use domain-independent features to classify some unknown gestures without modifying any code or data set. Lightweight computing resource consumption allows WiCross to respond in real-time. The experimental results show that WiCross can achieve an in-domain recognition accuracy of 93.54% and a cross-domain recognition accuracy of 93.11%

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“…On Widar 3.0 data sets, their model evaluation improved by an average of 3% to 12.7%. In 1D, WiTrace [33] used the synthetic signal to derive the phase of the hand-reflected signal and measured the phase change to obtain the distance of movement. For 2D space, WiTrace proposed the Kalman Filter to filter out the noise of tracking.…”

Section: Related Workmentioning

confidence: 99%

Air-CSL: Chinese Sign Language Recognition Based on the Commercial WiFi Devices

Hong-hong

Feng

Hao

et al. 2022

Wireless Communications and Mobile Computing

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Artificial intelligence and Internet of Things (IoT) devices are experiencing explosive growth. Currently, the commonly used gesture recognition methods are difficult to deploy and expensive, so this paper uses the Channel State Information (CSI) for Chinese sign language recognition. Aiming at the problems of current gesture recognition methods, such as strong personnel dependence, high computational resource consumption, and low robustness, we proposed a Chinese sign language gesture recognition method named Air-CSL. In this method, the Local Outlier Factor (LOF) removal algorithm and the Discrete Wavelet Transform (DWT) are used to reduce the noise in the data, and the subcarriers that best represent the gesture data are selected by principal component analysis. After denoising, mathematical statistics were extracted from the gesture waveform as the eigenvalues, and the features were fused by the Deep Restricted Boltzmann Machine (DBM). Finally, the result of gesture classification and recognition is obtained by the Gated Recurrent Unit (GRU). In this way, the prediction model realizes as well as the classification of sign language gestures. The results show that the proposed method can effectively recognize Chinese sign language gestures of different people in different environments and has good robustness.

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WiTrace: Centimeter-Level Passive Gesture Tracking Using OFDM Signals

Cited by 20 publications

References 29 publications

Contact-Free Multitarget Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis

Contact-Free Multitarget Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis

Cross-domain extendable gesture recognition system using WiFi signals

Air-CSL: Chinese Sign Language Recognition Based on the Commercial WiFi Devices

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