UltrasonicGS: A Highly Robust Gesture and Sign Language Recognition Method Based on Ultrasonic Signals

Abstract

With the global spread of the novel coronavirus, avoiding human-to-human contact has become an effective way to cut off the spread of the virus. Therefore, contactless gesture recognition becomes an effective means to reduce the risk of contact infection in outbreak prevention and control. However, the recognition of everyday behavioral sign language of a certain population of deaf people presents a challenge to sensing technology. Ubiquitous acoustics offer new ideas on how to perceive everyday behavior. The advantages of a low sampling rate, slow propagation speed, and easy access to the equipment have led to the widespread use of acoustic signal-based gesture recognition sensing technology. Therefore, this paper proposed a contactless gesture and sign language behavior sensing method based on ultrasonic signals—UltrasonicGS. The method used Generative Adversarial Network (GAN)-based data augmentation techniques to expand the dataset without human intervention and improve the performance of the behavior recognition model. In addition, to solve the problem of inconsistent length and difficult alignment of input and output sequences of continuous gestures and sign language gestures, we added the Connectionist Temporal Classification (CTC) algorithm after the CRNN network. Additionally, the architecture can achieve better recognition of sign language behaviors of certain people, filling the gap of acoustic-based perception of Chinese sign language. We have conducted extensive experiments and evaluations of UltrasonicGS in a variety of real scenarios. The experimental results showed that UltrasonicGS achieved a combined recognition rate of 98.8% for 15 single gestures and an average correct recognition rate of 92.4% and 86.3% for six sets of continuous gestures and sign language gestures, respectively. As a result, our proposed method provided a low-cost and highly robust solution for avoiding human-to-human contact.

An object detection system using array planar capacitive sensors

“…Specifically, it has a faster recognition rate. In terms of recognition accuracy, this system has higher recognition accuracy than the ones proposed by Wang et al [2] and Zhang et al [7] but is slightly lower than the one proposed by Wang et al [9] . Wang et al [9] significantly increased the consumption of system resources by using the dual LSTM algorithm, which posed a significant resource risk for embedded device operation.…”

“…Based on the recorded table above, it is obvious that the embedded system proposed in this paper has a high recognition accuracy, reaching over 98.92%. , Zhang et al [7] , and Wang et al [9] , the proposed system in this paper has significant characteristics. Specifically, it has a faster recognition rate.…”

“…However, depending on the application field and scenario, some tactile gesture recognition systems may not achieve optimal results, making the research of non-tactile gesture recognition significant. Non-tactile gesture recognition mainly includes depth cameras [5] , miniature radars [6] , WIFI [7,8] , ultrasound [9] , and others. The significant feature of non-tactile gesture recognition methods is the ability to recognize without palm contact, making non-tactile gesture recognition more applicable in daily life scenarios.…”