A Robust Sign Language Recognition System with Multiple Wi-Fi Devices

Shang, Jiacheng; Wu, Jie

doi:10.1145/3097620.3097624

Cited by 42 publications

(23 citation statements)

References 8 publications

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“…WiSee [5] [14], that is able to accurately detect the smoking activities by exploiting the impact of smoking on the CSI of WiFi. Shang et al propose a WiFi signal-based sign language recognition system called WiSign [15]. Different from other systerm, WiSign uses 3 WiFi devices to improve the recognition performance.…”

Section: A Hardware-based Methodsmentioning

confidence: 99%

“…In our experiment, there are only two classifiers, which lead to that the traditional combination algorithms not suitable for the challenges we are facing. In WiSign [15], Shang where P(y = 1| f (x)) indicates the probability that the sample under the condition of standard output value f (x) is the target class. A and B are parameters that need to be optimized, which can be obtained by using the training set for maximum likelihood estimation.…”

Section: Predictionmentioning

confidence: 99%

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Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals

Chen

et al. 2019

IEEE Access

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Recent research has shown that human motions and positions can be recognized through WiFi signals. The key intuition is that different motions and positions introduce different multi-path distortions in WiFi signals and generate different patterns in the time-series of channel state information (CSI). In this paper, we propose Wi-Motion, a WiFi-based human activities recognition system. Unlike existing systems, Wi-Motion adopts the amplitude and phase information extracted from the CSI sequence to construct the classifiers respectively, and combines the results using a combination strategy based on posterior probability. As the simulation results shows, Wi-Motion can recognize six human activities with the mean accuracy of 98.4%.

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Section: A Hardware-based Methodsmentioning

confidence: 99%

Section: Predictionmentioning

confidence: 99%

Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals

Chen

et al. 2019

IEEE Access

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“…Currently, the general classifiers include Dynamic Time Warping (DTW), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Hidden Markov Model (HMM), BP Neural Network (BPNN), Self-Organizing Map (SOM), etc. This article presents some typical behavior recognition applications, including daily behavior recognition [55]- [62], table tennis action recognition [63], bodyweight exercise recognition [64], danger pose detection [65], abnormal activity detection [66], falling detection [67]- [71], hand gesture recognition [72]- [81], sign language recognition [82], [83], sleep monitoring [84], respiration detection [85]- [88], lip reading and speech recognition [89], [90], keystroke detection [91], [92], writing recognition [93]- [95], sedentary behavior monitoring [96], smoking detection [97], crowd counting [98]- [103], step counting [104], [105], human presence detection [106]- [111], and user authentication [112]- [123].…”

Section: A Pattern-based Behavior Recognitionmentioning

confidence: 99%

“…Shang et al propose WiSign [83] and improve it [82] in 2017, two sign language recognition systems that recognize eight sign languages such as Hello, Thanks, Yes, No, etc. The main characteristics of WiSign are that this system leverages training data that solely have sparse labels to recognize gestures by using transfer learning and semi-supervised learning.…”

Section: C: Sign Language Recognitionmentioning

confidence: 99%

“…The experiments show that WiSign can achieve the mean prediction accuracies of 87.01% with the transfer learning-based approach and 87.38% with the semi-supervised learning-based approach for all participants, which are better than traditional SVM. In order to improve recognition accuracy, another laptop is introduced and different kernel functions of SVM is applied [82]. Meanwhile, for some complicated gestures whose signal often overlaps, a weighted voting system is employed.…”

Section: C: Sign Language Recognitionmentioning

confidence: 99%

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A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including patternbased, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions. INDEX TERMS Channel state information (CSI), deep learning, human behavior recognition, model, pattern, WiFi.

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Artificial intelligence driven Wi‐Fi CSI data mining: Focusing on the intrusion detection applications

Zhao

Bhuiyan

et al. 2022

Int J Communication

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SummaryIn the past several years, a series of breakthrough research advancements have been achieved by leveraging wireless signals such as Wi‐Fi in various emerging applications, including healthcare, behavior recognition, positioning, and target detection. Compared to traditional human behavior sensing methods, Wi‐Fi signals human behavior sensing technology has many advantages, including non‐line‐of‐sight, sensor device‐free sensing, passive sensing, ease of deployment, and no need for lights. Data mining undoubtedly plays a critical role in making Wi‐Fi‐based human behavior detection intelligent enough to facilitate convenient services and environments. We study Wi‐Fi signals mining using the data mining process and review the developmental process of Wi‐Fi data mining. This covers the methods of Wi‐Fi data mining, including signal acquisition, preprocessing, feature extraction to training, and classification. We then propose WHSecurity, a whole home intrusion detection and tracking system that is based on all of the methods covered above. Finally, WHSecurity includes a deep learning‐based data mining process called multiview learning for the decision‐making on intrusion detection and tracking. Experimental outcomes show that the WHSecurity approach performs superior in terms of intrusion detection and tracking performance.

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A Robust Sign Language Recognition System with Multiple Wi-Fi Devices

Cited by 42 publications

References 8 publications

Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals

Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals

A Survey on Human Behavior Recognition Using Channel State Information

Artificial intelligence driven Wi‐Fi CSI data mining: Focusing on the intrusion detection applications

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