WiCatch: A Wi-Fi Based Hand Gesture Recognition System

Tian, Zengshan; Wang, Jiacheng; Yang, Xiaolong; Zhou, Mu

doi:10.1109/access.2018.2814575

Cited by 73 publications

(39 citation statements)

References 28 publications

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“…Table 4 provides a summary of the models between human activity characteristics and critical parameters of wireless signal propagation. [36,69], Coarse-grained estimation [14,18,19,[23][24][25][26]37,40,42,43,46,47,49,50,[52][53][54] Distance (dLoS) Fresnel zone model [106][107][108][109][110] Direction Fresnel zone model [106], AoA with antenna array [11,15,65,[111][112][113][114][115] aw Signal Distance, Direction, Velocity mD-Track [116] 3.1. Phase…”

Section: Modeling Human Activity With Wireless Signalmentioning

confidence: 99%

Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities.

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Section: Modeling Human Activity With Wireless Signalmentioning

confidence: 99%

Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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“…WiGeR classified 13 hand gestures under different scenarios and achieved an average accuracy of 92% in several scenarios. Tian et al (2018b) presented a hand gesture recognition scheme that leverages the CSI of WiFi signals. The basic idea to build a virtual antenna from the reflected signals due to hand motions.…”

Section: Csimentioning

confidence: 99%

“…The proposed scheme was evaluated with 6 hand gestures and achieved an average accuracy of 97%. A hand gesture recognition method based on CSI, namely, WiCatch was presented (Tian et al 2018b). Wicatch also adopted SVM to classify nine hand gestures and achieved an accuracy of 96%.…”

Section: Csimentioning

confidence: 99%

Device-free human micro-activity recognition method using WiFi signals

Al-qaness

2019

Geo-spatial Information Science

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Human activity tracking plays a vital role in human-computer interaction. Traditional human activity recognition (HAR) methods adopt special devices, such as cameras and sensors, to track both macro-and micro-activities. Recently, wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment. This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information (CSI) of wireless signals. Different from existed CSI-based microactivity recognition methods, the proposed method extracts both amplitude and phase information from CSI, thereby providing more information and increasing detection accuracy. The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity. We applied a machine learning algorithm to recognize the proposed micro-activities. The proposed method has been evaluated in both line of sight (LOS) and none line of sight (NLOS) scenarios, and the empirical results demonstrate the effectiveness of the proposed method with several users.

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“…During the previous decade, KNN and SRC algorithms have been numerously used in various CSI-based device-free activity recognition models, as a stand-alone classifier. WiCatch [41] proposed WiFi-based hand gesture recognition system utilizing weak signals reflected from hands and the recognition is based on support vector machines (SVMs). Wi-Key [16] recognized keystrokes via CSI-waveform using the KNN classifier.…”

Section: Related Workmentioning

confidence: 99%

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

Akhtar

Wang

2019

Applied Sciences

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In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%.

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WiCatch: A Wi-Fi Based Hand Gesture Recognition System

Cited by 73 publications

References 28 publications

Human Activity Sensing with Wireless Signals: A Survey

Human Activity Sensing with Wireless Signals: A Survey

Device-free human micro-activity recognition method using WiFi signals

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

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