A Synchronous Acquisition System of Ultrasound, sEMG and IMU for Human Motion Prediction

Yifan Liu; Zongtian Yin; Hongyu Yang; Xingchen Yang; Honghai Liu

doi:10.1007/978-3-031-13835-5_8

A Synchronous Acquisition System of Ultrasound, sEMG and IMU for Human Motion Prediction

Yifan Liu
, Zongtian Yin
, Hongyu Yang
, Xingchen Yang
, Honghai Liu^*

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Shanghai Jiao Tong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

At present, due to the limited information, the single man-machine interface control has some defects in human motion prediction, such as low accuracy and poor robustness. In this work, a multi-modal real-time acquisition system that combines surface electromyography (sEMG), inertial measurement (IMU) and A-mode ultrasound (AUS) information is used to upper limb motion prediction. The device we developed can simultaneously collect three kinds of signals, eliminating the operation of manual alignment. sEMG can reflect the electrical activity of muscle contraction, AUS can detect the deformation of deep muscles, and IMU can obtain information such as the speed and acceleration of the limbs. One healthy subjects participated in the experiment. The results show that the motion prediction accuracy of three modal information fusion is higher than that of any one or two information fusion, which is expected to provide a better control method in exoskeleton or prosthesis.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings
Editors	Honghai Liu, Weihong Ren, Zhouping Yin, Lianqing Liu, Li Jiang, Guoying Gu, Xinyu Wu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	79-89
Number of pages	11
ISBN (Print)	9783031138348
DOIs	https://doi.org/10.1007/978-3-031-13835-5_8
State	Published - 2022
Externally published	Yes
Event	15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 - Harbin, China Duration: 1 Aug 2022 → 3 Aug 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13457 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Intelligent Robotics and Applications, ICIRA 2022
Country/Territory	China
City	Harbin
Period	1/08/22 → 3/08/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

A-mode ultrasound
IMU
Motion prediction
sEMG

Access to Document

10.1007/978-3-031-13835-5_8

Cite this

Liu, Y., Yin, Z., Yang, H., Yang, X., & Liu, H. (2022). A Synchronous Acquisition System of Ultrasound, sEMG and IMU for Human Motion Prediction. In H. Liu, W. Ren, Z. Yin, L. Liu, L. Jiang, G. Gu, & X. Wu (Eds.), Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings (pp. 79-89). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13457 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-13835-5_8

Liu, Yifan ; Yin, Zongtian ; Yang, Hongyu et al. / A Synchronous Acquisition System of Ultrasound, sEMG and IMU for Human Motion Prediction. Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. editor / Honghai Liu ; Weihong Ren ; Zhouping Yin ; Lianqing Liu ; Li Jiang ; Guoying Gu ; Xinyu Wu. Springer Science and Business Media Deutschland GmbH, 2022. pp. 79-89 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{26ee1b6b3bd44dea8ae05182277024d3,

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abstract = "At present, due to the limited information, the single man-machine interface control has some defects in human motion prediction, such as low accuracy and poor robustness. In this work, a multi-modal real-time acquisition system that combines surface electromyography (sEMG), inertial measurement (IMU) and A-mode ultrasound (AUS) information is used to upper limb motion prediction. The device we developed can simultaneously collect three kinds of signals, eliminating the operation of manual alignment. sEMG can reflect the electrical activity of muscle contraction, AUS can detect the deformation of deep muscles, and IMU can obtain information such as the speed and acceleration of the limbs. One healthy subjects participated in the experiment. The results show that the motion prediction accuracy of three modal information fusion is higher than that of any one or two information fusion, which is expected to provide a better control method in exoskeleton or prosthesis.",

keywords = "A-mode ultrasound, IMU, Motion prediction, sEMG",

author = "Yifan Liu and Zongtian Yin and Hongyu Yang and Xingchen Yang and Honghai Liu",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 ; Conference date: 01-08-2022 Through 03-08-2022",

year = "2022",

doi = "10.1007/978-3-031-13835-5\_8",

language = "英语",

isbn = "9783031138348",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "79--89",

editor = "Honghai Liu and Weihong Ren and Zhouping Yin and Lianqing Liu and Li Jiang and Guoying Gu and Xinyu Wu",

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Liu, Y, Yin, Z, Yang, H, Yang, X & Liu, H 2022, A Synchronous Acquisition System of Ultrasound, sEMG and IMU for Human Motion Prediction. in H Liu, W Ren, Z Yin, L Liu, L Jiang, G Gu & X Wu (eds), Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13457 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 79-89, 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022, Harbin, China, 1/08/22. https://doi.org/10.1007/978-3-031-13835-5_8

A Synchronous Acquisition System of Ultrasound, sEMG and IMU for Human Motion Prediction. / Liu, Yifan; Yin, Zongtian; Yang, Hongyu et al.
Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. ed. / Honghai Liu; Weihong Ren; Zhouping Yin; Lianqing Liu; Li Jiang; Guoying Gu; Xinyu Wu. Springer Science and Business Media Deutschland GmbH, 2022. p. 79-89 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13457 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Synchronous Acquisition System of Ultrasound, sEMG and IMU for Human Motion Prediction

AU - Liu, Yifan

AU - Yin, Zongtian

AU - Yang, Hongyu

AU - Yang, Xingchen

AU - Liu, Honghai

PY - 2022

Y1 - 2022

N2 - At present, due to the limited information, the single man-machine interface control has some defects in human motion prediction, such as low accuracy and poor robustness. In this work, a multi-modal real-time acquisition system that combines surface electromyography (sEMG), inertial measurement (IMU) and A-mode ultrasound (AUS) information is used to upper limb motion prediction. The device we developed can simultaneously collect three kinds of signals, eliminating the operation of manual alignment. sEMG can reflect the electrical activity of muscle contraction, AUS can detect the deformation of deep muscles, and IMU can obtain information such as the speed and acceleration of the limbs. One healthy subjects participated in the experiment. The results show that the motion prediction accuracy of three modal information fusion is higher than that of any one or two information fusion, which is expected to provide a better control method in exoskeleton or prosthesis.

AB - At present, due to the limited information, the single man-machine interface control has some defects in human motion prediction, such as low accuracy and poor robustness. In this work, a multi-modal real-time acquisition system that combines surface electromyography (sEMG), inertial measurement (IMU) and A-mode ultrasound (AUS) information is used to upper limb motion prediction. The device we developed can simultaneously collect three kinds of signals, eliminating the operation of manual alignment. sEMG can reflect the electrical activity of muscle contraction, AUS can detect the deformation of deep muscles, and IMU can obtain information such as the speed and acceleration of the limbs. One healthy subjects participated in the experiment. The results show that the motion prediction accuracy of three modal information fusion is higher than that of any one or two information fusion, which is expected to provide a better control method in exoskeleton or prosthesis.

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AN - SCOPUS:85136139596

SN - 9783031138348

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings

A2 - Liu, Honghai

A2 - Ren, Weihong

A2 - Yin, Zhouping

A2 - Liu, Lianqing

A2 - Jiang, Li

A2 - Gu, Guoying

A2 - Wu, Xinyu

PB - Springer Science and Business Media Deutschland GmbH

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Y2 - 1 August 2022 through 3 August 2022

ER -

Liu Y, Yin Z, Yang H, Yang X, Liu H. A Synchronous Acquisition System of Ultrasound, sEMG and IMU for Human Motion Prediction. In Liu H, Ren W, Yin Z, Liu L, Jiang L, Gu G, Wu X, editors, Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 79-89. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-13835-5_8