A Study on Recognition Algorithm of Ultrasonic Sensing Gesture Based on Improved Hidden Markov Model

Liu, Dianting; Zhang, Chenguang; Huang, Kangzheng; Wu, Danling; Gege, Zhao

doi:10.1109/cac51589.2020.9327822

Cited by 1 publication

(1 citation statement)

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“…Compared with the gesture recognition experiment of reading the information on the display screen of an intelligent device designed by authors before [31], the recognition accuracy of the teleoperation gesture and reading gesture is compared and analysed. From the comparison of Table 4, it can be concluded that the recognition algorithm of the teleoperation gesture based on two different frequencies of ultrasonic waves are more accurate than the recognition algorithm of the reading gesture based on one frequency of the ultrasonic wave in recognition of simple gestures, while lower in the recognition rate of the complex gesture.…”

Section: Typesmentioning

confidence: 99%

A study on the algorithm of ultrasonic detection and recognition based on DAG‐SVMs mixed HMM of teleoperation gestures for intelligent manufacturing devices

Liu

Zhang

et al. 2021

IET Collab Intel Manufact

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Remote control for the position and status of a machine or an equipment can often be teleoperated by gestures in an intelligent manufacturing environment. In order to solve the problems that gestures with two directions such as left and right cannot be detected by single ultrasonic frequency, double different ultrasonic frequencies are used to detect gestures by the Doppler shift, and an algorithm of the recognition gesture based on the DAG‐SVMs mixed Hidden Markov Model (HMM) is proposed to identify and classify the extracted feature sequences. Thus, four more types of gestures are expanded other than that of reading display screen information, and the comparative experiments to classify and recognise gestures of teleoperation are made with DAG‐SVMs, the HMM, the DAG‐SVMs mixed HMM, and other improved HMM algorithms. The test results have shown that the mean rate of gesture recognition for the algorithm based on the DAG‐SVMs mixed HMM is 94.917%, which is 9.497% higher than that of the unimproved HMM, and its recognition accuracy of complex teleoperation gestures is improved by 2.3% compared with other improved HMM algorithms. The experimental results show that the DAG‐SVMs mixed HMM algorithm has a good effect on recognition for the gestures of teleoperation and it can perform gesture recognition accurately.

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Section: Typesmentioning

confidence: 99%