Wang Hai, Tao Qing, Zhang Xiaodong
School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China.
Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2024 Jan 20;24(2):0. doi: 10.3390/s24020660.
Human-machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects' dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses.
人机接口技术从根本上受到运动解码灵活性的限制。同时和成比例控制可以极大地提高智能假肢的灵活性和敏捷性。在本研究中，提出了一种使用集成学习来解决角度解码问题的新模型。最终，设计了七种用于从表面肌电图（sEMG）信号进行角度解码的模型。利用功能任务期间记录的sEMG来估计掌指（MCP）关节五个角度的运动学。通过皮尔逊相关系数（CC）评估估计性能。在本研究中，结合CatBoost和LightGBM的综合模型是该任务的最佳模型，其平均CC值和均方根误差（RMSE）分别为0.897和7.09。受试者数据集所有测试场景的CC均值和RMSE均值均优于高斯过程模型的结果，且差异显著。此外，该研究提出了一个完整的流程，即使用集成学习为手部运动识别任务构建一个高性能的角度解码系统。该领域的研究人员或工程师可以通过此流程快速找到最适合角度解码的集成学习模型，与传统深度学习模型相比，其参数更少，对训练数据的要求也更少。总之，所提出的集成学习方法具有实现未来手部假肢同时和成比例控制（SPC）的潜力。
