Deep transfer learning for gesture recognition with WiFi signals

Bu, Qirong; Yang, Gang; Ming, Xingxia; Zhang, Tuo; Feng, Jufu; Zhang, Jing

doi:10.1007/s00779-019-01360-8

Cited by 35 publications

(11 citation statements)

References 30 publications

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“…Building upon this representation, researchers have attempted to overcome the inherent limitations of Wi-Fi signal sensing, such as low time resolution, vulnerability to interference, and narrow bandwidth. Qirong Bu et al [22] extracted gesture segments based on changes in CSI amplitude and transformed the problem of Wi-Fi-based gesture recognition into an image classification task by representing CSI streams as image matrices and inputting them into deep learning networks for recognition. Zhanjun Hao et al [23] established a correlation mapping between the amplitude and phase difference information of subcarrier levels in wireless signals and sign language gestures.…”

Section: Research Methods and Current Situation 21 Electromagnetic Wa...mentioning

confidence: 99%

Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

Zhou

Wang

et al. 2023

Electronics

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Gesture recognition, as a core technology of human–computer interaction, has broad application prospects and brings new technical possibilities for smart homes, medical care, sports training, and other fields. Compared with the traditional human–computer interaction models based on PC use with keyboards and mice, gesture recognition-based human–computer interaction modes can transmit information more naturally, flexibly, and intuitively, which has become a research hotspot in the field of human–computer interaction in recent years. This paper described the current status of gesture recognition technology, summarized the principles and development history of electromagnetic wave sensor recognition, stress sensor recognition, electromyographic sensor recognition, and visual sensor recognition, and summarized the improvement of this technology by researchers in recent years through the direction of sensor structure, selection of characteristic signals, the algorithm of signal processing, etc. By sorting out and comparing the typical cases of the four implementations, the advantages and disadvantages of each implementation and the application scenarios were discussed from the two aspects of dataset size and accuracy. Based on the abovementioned discussion, the problems and challenges of current gesture recognition technology were discussed in terms of the biocompatibility of sensor structures, wearability and adaptability, stability, robustness, and crossover of signal acquisition and analysis algorithms, and the future development directions in this field were proposed.

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Section: Research Methods and Current Situation 21 Electromagnetic Wa...mentioning

confidence: 99%

Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

Zhou

Wang

et al. 2023

Electronics

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“…Existing researches have enabled parameters migration between different domains, such as ref. [36]. They trained a CSI feature image classifier using pre‐trained parameters, which were gained through training the model in the dataset of ImageNet.…”

Section: Related Workmentioning

confidence: 99%

UltraDigit: An ultrasound signal‐based in‐air digit input system via transfer learning

Yang,

Zhang,

Wang

2023

IET Radar Sonar & Navi

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With the popularity of smart devices, people pay more attention to the experience of text input. The authors propose an in‐air digit input system, namely UltraDigit, which enables users to input digits in the air using acoustic signal via transfer learning. Different from other technologies, UltraDigit can obtain the finger movement through a speaker and a microphone built in the smartphone, which does not need the assistance of other hardware. In addition, the authors use the idea of transfer learning with a small amount of data to train classification model, which achieves good performance with low training cost (such as time cost and data cost). Experimental results show the average accuracy of digit recognition is 92.9%, which verifies the validity of the ultrasound signal‐based contactless digit input.

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“…In the method, the component corresponding to the user's hand movement speed is extracted according to Doppler frequency shift, and the human gestures are recognized without knowing the target localization. Bu et al [22] proposed a gesture recognition method based on deep transfer learning. Firstly, the CSI stream representing gestures are captured, and the gesture fragment data is extracted by using the amplitude change of CSI.…”

Section: Gesture Recognitionmentioning

confidence: 99%

Small CSI Samples-Based Activity Recognition: A Deep Learning Approach Using Multidimensional Features

Tian

Chen

et al. 2021

Security and Communication Networks

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With the emergence of tools for extracting CSI data from commercial WiFi devices, CSI-based device-free activity recognition technology has developed rapidly and has been widely used in security monitoring, smart home, medical monitoring, and other fields. However, the existing CSI-based activity recognition algorithms need a large number of training samples to obtain the ideal recognition accuracy. To solve the problem, an attention-based bidirectional LSTM method using multidimensional features (called MF-ABLSTM method) is proposed. In this method, the signal preprocessing and continuous wavelet transform algorithms are used to construct time-frequency matrix, the sample entropy is used to characterize the statistical feature of CSI amplitudes, the energy difference at a fixed time interval is used to characterize the time-domain feature of activities, and the energy distribution of different frequency components is used to characterize the frequency-domain feature of activities. By expanding the training samples with the proposed tensor prediction algorithm, the accurate activity recognition can be realized with only a few samples. A large number of experiments verify the good performance of MF-ABLSTM method.

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Deep transfer learning for gesture recognition with WiFi signals

Cited by 35 publications

References 30 publications

Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

UltraDigit: An ultrasound signal‐based in‐air digit input system via transfer learning

Small CSI Samples-Based Activity Recognition: A Deep Learning Approach Using Multidimensional Features

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