We can hear you with Wi-Fi!

Wang, Guanhua; Zou, Yongpan; Zhou, Zimu; Wu, Kaishun; Ni, Lionel M.

doi:10.1145/2639108.2639112

Cited by 265 publications

(178 citation statements)

References 30 publications

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“…This is also fuelled by the availability of API to query CSI from WiFi chipsets such as Intel 5300 [5] and Atheros 9390 [23]. CSI has been used for many pattern recognition problems, including localisation [24], human detection [45], activity recognition [29,27], fine-grained gesture recognition [15] and to "lip-read" [28]. As mentioned in Section 1, our work differs from earlier work on using CSI for activity recognition in that we take RFI into consideration while earlier work did not.…”

Section: Radio Based Pattern Recognitionmentioning

confidence: 99%

Radio-based device-free activity recognition with radio frequency interference

Wei

Yang

et al. 2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

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Activity recognition is an important component of many pervasive computing applications. Device-free activity recognition has the advantage that it does not have the privacy concern of using cameras and the subjects do not have to carry a device on them. Recently, it has been shown that channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We conduct experiments in environments without and with RFI. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments shows that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. Our evaluation shows the proposed method can improve up to 10% true detection rate in the presence of RFI. We also study the impact of bandwidth on activity recognition performance. We show that with a channel bandwidth of 20 MHz (which is used by WiFi), it is possible to achieve a good activity recognition accuracy when RFI is present.

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Section: Radio Based Pattern Recognitionmentioning

confidence: 99%

Radio-based device-free activity recognition with radio frequency interference

Wei

Yang

et al. 2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

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“…Recently, CSI has been extended to recognize human motions such as fall detection [27], daily activity recognition [28], micro-movement recognition [29], and gesture recognition [30,31]. Nandakumar et al [30] presented a hand gesture recognition system that can identify four hand gestures with an accuracy of 91%, and 89% in LOS and in a backpack, respectively.…”

Section: Csimentioning

confidence: 99%

WiGeR: WiFi-Based Gesture Recognition System

Al-qaness

2016

IJGI

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Abstract:Recently, researchers around the world have been striving to develop and modernize human-computer interaction systems by exploiting advances in modern communication systems. The priority in this field involves exploiting radio signals so human-computer interaction will require neither special devices nor vision-based technology. In this context, hand gesture recognition is one of the most important issues in human-computer interfaces. In this paper, we present a novel device-free WiFi-based gesture recognition system (WiGeR) by leveraging the fluctuations in the channel state information (CSI) of WiFi signals caused by hand motions. We extract CSI from any common WiFi router and then filter out the noise to obtain the CSI fluctuation trends generated by hand motions. We design a novel and agile segmentation and windowing algorithm based on wavelet analysis and short-time energy to reveal the specific pattern associated with each hand gesture and detect duration of the hand motion. Furthermore, we design a fast dynamic time warping algorithm to classify our system's proposed hand gestures. We implement and test our system through experiments involving various scenarios. The results show that WiGeR can classify gestures with high accuracy, even in scenarios where the signal passes through multiple walls.

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“…For example, detecting if a shopper is reading the item label or puts the item in a cart requires fine-grained CSI fingerprinting. In recent work such as [9,10], such fine-grained activity recognition is shown to be feasible with CSI, however, further work is required in the context of physical analytics.…”

Section: Potential and Limitationsmentioning

confidence: 99%

Analyzing Shopper's Behavior through WiFi Signals

Zeng

Pathak

Mohapatra

2015

Proceedings of the 2nd Workshop on Workshop on Physical Analytics

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Substantial progress in WiFi-based indoor localization has proven that pervasiveness of WiFi can be exploited beyond its traditional use of internet access to enable a variety of sensing applications. Understanding shopper's behavior through physical analytics can provide crucial insights to the business owner in terms of e↵ectiveness of promotions, arrangement of products and e ciency of services. However, analyzing shopper's behavior and browsing patterns is challenging. Since video surveillance can not used due to high cost and privacy concerns, it is necessary to design novel techniques that can provide accurate and e cient view of shopper's behavior. In this work, we propose WiFi-based sensing of shopper's behavior in a retail store. Specifically, we show that various states of a shopper such as standing near the entrance to view a promotion or walking quickly to proceed towards the intended item can be accurately classified by profiling Channel State Information (CSI) of WiFi. We recognize a few representative states of shopper's behavior at the entrance and inside the store, and show how CSI-based profile can be used to detect that a shopper is in one of the states with very high accuracy (⇡ 90%). We discuss the potential and limitations of CSI-based sensing of shopper's behavior and physical analytics in general.

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We can hear you with Wi-Fi!

Cited by 265 publications

References 30 publications

Radio-based device-free activity recognition with radio frequency interference

Radio-based device-free activity recognition with radio frequency interference

WiGeR: WiFi-Based Gesture Recognition System

Analyzing Shopper's Behavior through WiFi Signals

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