<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the development of artificial intelligence and robotics technology, the combination of artificial intelligence and medical device research and development has been promoted, which is an important product of the development of artificial intelligence. In this paper, the general structure of the intelligent medical robot is designed by combining artificial intelligence technology and robotics-related technology. Then, the binocular vision function of the robot was realized by visually acquiring the image of the target object, 3D reconstruction of the target object, and combining the SIFT image recognition algorithm and target tracking algorithm. Then, a new speech recognition algorithm was constructed to realize the human-robot interaction function with the medical robot based on the deep learning Transforme network after the construction of the human acoustic model. Finally, the designed intelligent medical robot was tested, and its overall performance was evaluated. The results show that the recognition errors of the intelligent medical robot on the features of the items are all within 0.05, the recognition errors on the features of the human body are within 0.2, and the speed of the target tracking is between 6km/h and 16km/h. The average recognition accuracy of the medical robot for voice commands is about 0.9, the recognition time is about 0.7s, the normal working rate of each function is more than 0.99, and the test speed is within 2s.</p>
</abstract>

With the development of artificial intelligence and robotics technology, the combination of artificial intelligence and medical device research and development has been promoted, which is an important product of the development of artificial intelligence. In this paper, the general structure of the intelligent medical robot is designed by combining artificial intelligence technology and robotics-related technology. Then, the binocular vision function of the robot was realized by visually acquiring the image of the target object, 3D reconstruction of the target object, and combining the SIFT image recognition algorithm and target tracking algorithm. Then, a new speech recognition algorithm was constructed to realize the human-robot interaction function with the medical robot based on the deep learning Transforme network after the construction of the human acoustic model. Finally, the designed intelligent medical robot was tested, and its overall performance was evaluated. The results show that the recognition errors of the intelligent medical robot on the features of the items are all within 0.05, the recognition errors on the features of the human body are within 0.2, and the speed of the target tracking is between 6km/h and 16km/h. The average recognition accuracy of the medical robot for voice commands is about 0.9, the recognition time is about 0.7s, the normal working rate of each function is more than 0.99, and the test speed is within 2s.

A study on the application of artificial intelligence in the design of intelligent medical robots

University of Shanghai for Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the development of artificial intelligence and robotics technology, the combination of artificial intelligence and medical device research and development...

A study on the application of artificial intelligence in the design of intelligent medical robots

Pubblicato online: 06 dic 2023

Pagine: -

Ricevuto: 15 feb 2023

Accettato: 16 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01388

Parole chiave
Image recognition algorithm, Target tracking algorithm, Human-robot interaction, Artificial intelligence, Medical robotics

© 2023 Ke Miao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A study on the application of artificial intelligence in the design of intelligent medical robots

Pubblicato online: 06 dic 2023

Pagine: -

Ricevuto: 15 feb 2023

Accettato: 16 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01388

Parole chiaveImage recognition algorithm, Target tracking algorithm, Human-robot interaction, Artificial intelligence, Medical robotics

© 2023 Ke Miao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Image recognition algorithm, Target tracking algorithm, Human-robot interaction, Artificial intelligence, Medical robotics