Multi-layer CNN-LSTM network with self-attention mechanism for robust estimation of nonlinear uncertain systems

Lin Liu; Jun Feng; Jiwei Li; Wanxin Chen; Zhizhong Mao; Xiaowei Tan

doi:10.3389/fnins.2024.1379495

Multi-layer CNN-LSTM network with self-attention mechanism for robust estimation of nonlinear uncertain systems

Front Neurosci. 2024 Apr 4:18:1379495. doi: 10.3389/fnins.2024.1379495. eCollection 2024.

Authors

Lin Liu¹, Jun Feng^{1

2}, Jiwei Li^{2

3

4}, Wanxin Chen^{2

3

4}, Zhizhong Mao¹, Xiaowei Tan^{2

3}

Affiliations

¹ College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
² State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China.
³ Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China.
⁴ University of Chinese Academy of Sciences, Beijing, China.

Abstract

Introduction: With the help of robot technology, intelligent rehabilitation of patients with lower limb motor dysfunction caused by stroke can be realized. A key factor constraining the clinical application of rehabilitation robots is how to realize pattern recognition of human movement intentions by using the surface electromyography (sEMG) sensors to ensure unhindered human-robot interaction.

Methods: A multilayer CNN-LSTM prediction network incorporating the self-attention mechanism (SAM) is proposed, in this paper, which can extract and learn the periodic and trend characteristics of the sEMG signals, and realize the accurate autoregressive prediction of the human motion information. Firstly, the multilayer CNN-LSTM network utilizes the CNN layer for initial feature extraction of data, and the LSTM network is used to improve the enhancement of the historical time-series features. Then, the SAM is used to improve the global feature extraction performance and parallel computation speed of the network.

Results: In comparison with existing test is carried out using actual data from five healthy subjects as well as a clinical hemiplegic patient to verify the superiority and practicality of the proposed algorithm. The results show that most of the model's prediction R > 0.9 for different motion states of healthy subjects; in the experiments oriented to the motion characteristics of patient subjects, the angle prediction results of R > 0.99 for the untrained data on the affected side, which proves that our proposed model also has a better effect on the angle prediction of the affected side.

Discussion: The main contribution of this paper is to realize continuous motion estimation of ankle joint for healthy and hemiplegic individuals under non-ideal conditions (weak sEMG signals, muscle fatigue, high muscle tension, etc.), which improves the pattern recognition accuracy and robustness of the sEMG sensor-based system.

Keywords: machine learning network; nonlinear systems; robust estimation; surface electromyography signal; uncertainties.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was partly supported by the National Natural Science Foundation of China under grant numbers 62333007, 62103406, and U22A2067, and supported by the Applied Basic Research Program Project of Liaoning Province under grant number 2022JH2/101300102.