Towards an end-to-end isolated and continuous deep gesture recognition process

Mahmoud, Rihem; Belgacem, Selma; Omri, Mohamed Nazih

doi:10.1007/s00521-022-07165-w

Cited by 8 publications

(1 citation statement)

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“…Mahmoud et al. [ 19 ] presented a robust deep learning approach for characterizing, segmenting, and classifying isolated and continuous gesture sequences using depth, RGB, and grayscale input data. The proposed process was suitable for both full human action and gesture recognition.…”

Section: Related Workmentioning

confidence: 99%

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

Wang

Hao

Dang

et al. 2023

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%