WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals

Niu, Kai; Zhang, Fusang; Jiang, Yuhang; Xiong, Jie; Lv, Qin; Zeng, Youwei; Zhang, Daqing

doi:10.1109/jiot.2019.2934904

Cited by 60 publications

(21 citation statements)

References 48 publications

(58 reference statements)

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“…Since the antennas in the same LTE adapter share the same clock, they have the same CFO, SFO and so on. Thus, we introduce the CSI ratio [15,33] between two antennas as a new base signal for LTE-based sensing to suppress the impulse noise in amplitude and eliminate these phase random offsets in CSI of LTE signals. The CSI ratio is defined as follows:…”

Section: Csi Ratiomentioning

confidence: 99%

“…More importantly, we can observe that CSI ratio circles around approximately 7 times in the complex plane as the bucket moves 50cm in Figure 4c. It has been proved in [15,33] that the CSI ratio induced by a single moving (reflective) target have following properties:…”

Section: Csi Ratiomentioning

confidence: 99%

“…Dynamic hand gestures usually provide richer information than static gestures because of the incorporation of motion. Existing approaches to device-free hand gesture recognition mainly fall into four categories: visionbased [13,23], acoustic-based [22,28], Radio Frequency (RF) based using specialized devices such as mmWave and Ultra-wideband radars [17,38] and WiFi-based solutions [2,4,15,37,40]. Among them, WiFi-based solutions utilize Channel State Information (CSI) extracted from WiFi signals received at a mobile terminal to recognize various hand gestures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Dynamic Hand Gesture Interaction using LTE Terminals

Chen

Niu

Deng

et al. 2020

2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

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Device-free hand gesture is one of the most natural ways to interact with everyday objects. However, existing WiFi-based gesture recognition solutions are typically restricted to indoor environments due to limited outdoor coverage. Furthermore, to achieve high sampling rates, they may interfere with normal data transmissions. In this paper, we aim to develop a robust dynamic gesture interaction system that can be ubiquitously deployed using Long-term Evolution (LTE) mobile terminals. Through both empirical studies and in-depth analysis using the Fresnel zone model, we reveal the key factors that contribute to the repeatability and discernibility of gestures. We show that the optimal location and orientation to perform gestures indeed exist and can be identified without prior knowledge of the position of LTE base stations (BSs) relative to a terminal. Guided by the design principles derived from Fresnel zone characteristics around a 4G terminal, we design highly repeatable and discernible gestures with salient received signal profiles. A gesture interaction system has been developed and implemented to achieve robust recognition with this careful design. Extensive experiments have been conducted in both indoor and outdoor environments, for different relative placements of mobile terminal and BS, and with different users. The proposed system can automatically identify the direction of BSs with a median error of less than 15 degrees and achieve gesture recognition accuracy as high as 98% in all scenarios without the need to acquire any training data. * Corresponding author CCS CONCEPTS • Human-centered computing → Ubiquitous computing; Activity centered design; Gestural input; Ubiquitous and mobile computing systems and tools; • Information systems → Mobile information processing systems.

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Section: Csi Ratiomentioning

confidence: 99%

Section: Csi Ratiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Dynamic Hand Gesture Interaction using LTE Terminals

Chen

Niu

Deng

et al. 2020

2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

Self Cite

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“…Despite its popularity, existing Wi-Fi-based sensing technologies are constrained by a practical challenge which we refer to as the position-dependency challenge: many existing solutions use machine learning or deep learningbased techniques to learn and extract features from the CSI signals that can be used to recognize human actions and activities. Unfortunately, these features capture information related to not only the action itself but also the location and orientation of the action [12,32,39]. Therefore, existing solutions can not recognize actions in a location/orientation-independent manner.…”

Section: Introductionmentioning

confidence: 99%

Towards Position-Independent Sensing for Gesture Recognition with Wi-Fi

Gao

Zhang

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Past decades have witnessed the extension of the Wi-Fi signals as a useful tool sensing human activities. One common assumption behind it is that there is a one-to-one mapping between human activities and Wi-Fi received signal patterns. However, this assumption does not hold when the user conducts activities in different locations and orientations. Actually, the received signal patterns of the same activity would become inconsistent when the relative location and orientation of the user with respect to transceivers change, leading to unstable sensing performance. This problem is known as the position-dependent problem, hindering the actual deployment of Wi-Fi-based sensing applications. In this paper, to tackle this fundamental problem, we develop a new position-independent sensing strategy and use gesture recognition as an application example to demonstrate its effectiveness. The key idea is to shift our observation from the traditional transceiver view to the hand-oriented view, and extract features that are irrespective of position-specific factors. Following the strategy, we design a position-independent feature, denoted as Motion Navigation Primitive(MNP). MNP captures the pattern of moving direction changes of the hand, which shares consistent patterns when the user performs the same gesture with different position-specific factors. By analyzing the pattern of MNP, we convert gestures into sequences of strokes (e.g, line, arc and corner) which makes them easy to be recognized. We build a prototype WiFi gesture recognition system, i.e., WiGesture to validate the effectiveness of the proposed strategy. Experiments show that our system can outperform the start-of-arts significantly in different settings. Given its novelty and superiority, we believe the proposed method symbolizes a major step towards gesture recognition and would inspire other solutions to position-independent activity recognition in the future.

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“…Wi-Sign [ 26 ] uses three WiFi transceivers to associate gesture movements with CSI signal waveform changes and realizes the recognition of eight commonly used sign language gestures. WiMorse [ 27 ] uses CSI to sense subtle finger movements and achieves 95% recognition accuracy for Morse codes generated by fingers.…”

Section: Introductionmentioning

confidence: 99%

Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information

Hao

Duan

Dang

et al. 2020

Sensors

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In recent years, with the development of wireless sensing technology and the widespread popularity of WiFi devices, human perception based on WiFi has become possible, and gesture recognition has become an active topic in the field of human-computer interaction. As a kind of gesture, sign language is widely used in life. The establishment of an effective sign language recognition system can help people with aphasia and hearing impairment to better interact with the computer and facilitate their daily life. For this reason, this paper proposes a contactless fine-grained gesture recognition method using Channel State Information (CSI), namely Wi-SL. This method uses a commercial WiFi device to establish the correlation mapping between the amplitude and phase difference information of the subcarrier level in the wireless signal and the sign language action, without requiring the user to wear any device. We combine an efficient denoising method to filter environmental interference with an effective selection of optimal subcarriers to reduce the computational cost of the system. We also use K-means combined with a Bagging algorithm to optimize the Support Vector Machine (SVM) classification (KSB) model to enhance the classification of sign language action data. We implemented the algorithms and evaluated them for three different scenarios. The experimental results show that the average accuracy of Wi-SL gesture recognition can reach 95.8%, which realizes device-free, non-invasive, high-precision sign language gesture recognition.

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WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals

Cited by 60 publications

References 48 publications

Robust Dynamic Hand Gesture Interaction using LTE Terminals

Robust Dynamic Hand Gesture Interaction using LTE Terminals

Towards Position-Independent Sensing for Gesture Recognition with Wi-Fi

Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information

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