Abstract
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.
Highlights
As intelligent devices have been integrated into thousands of households, the traditional means of human-computer interaction can no longer meet the growing needs of users
The main contributions of this work are as follows: We propose a Channel State Information (CSI)-based device recognition method for sign language actions, Wi-SL
In the Atheros AR9580 NIC chip and 40Mhz bandwidth channel environment, In this paper, in the Atheros AR9580 NIC chip and 40Mhz bandwidth channel environment, the the subcarrier index sequence is from number 1 to 114, and the fine-graininess of CSI is expressed on subcarrier index sequence is from number 1 to 114, and the fine-graininess of CSI is expressed on each subcarrier in the frequency domain, with different subcarriers reflecting the different effects of each subcarrier in the frequency domain, with different subcarriers reflecting the different effects of sign language actions on the wireless channel
Summary
As intelligent devices have been integrated into thousands of households, the traditional means of human-computer interaction can no longer meet the growing needs of users. The gesture recognition technology using special sensors can directly obtain fine-grained hand and finger motion data and achieve higher recognition accuracy, due to the need for users to wear additional devices, limited to the sensing distance of the sensor and expensive deployment and maintenance costs, it cannot be used on a large scale. The main contributions of this work are as follows: We propose a CSI-based device recognition method for sign language actions, Wi-SL.
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