Abstract
Electromyography (EMG) signals are widely used for predicting body joint angles through machine learning (ML) and deep learning (DL) methods. However, these approaches often face challenges such as limited real-time applicability, non-representative test conditions, and the need for large datasets to achieve optimal performance. This paper presents a transfer-learning framework for knee joint angle prediction that requires only a few gait cycles from new subjects. Three datasets - Georgia Tech, the University of California Irvine (UCI), and the Sharif Mechatronic Lab Exoskeleton (SMLE) - containing four EMG channels relevant to knee motion were utilized. A lightweight attention-based CNN-LSTM model was developed and pre-trained on the Georgia Tech dataset, then transferred to the UCI and SMLE datasets. The proposed model achieved Normalized Mean Absolute Errors (NMAE) of 6.8 percent and 13.7 percent for one-step and 50-step predictions on abnormal subjects using EMG inputs alone. Incorporating historical knee angles reduced the NMAE to 3.1 percent and 3.5 percent for normal subjects, and to 2.8 percent and 7.5 percent for abnormal subjects. When further adapted to the SMLE exoskeleton with EMG, kinematic, and interaction force inputs, the model achieved 1.09 percent and 3.1 percent NMAE for one- and 50-step predictions, respectively. These results demonstrate robust performance and strong generalization for both short- and long-term rehabilitation scenarios.
Abstract (translated)
肌电图（EMG）信号通过机器学习（ML）和深度学习（DL）方法被广泛用于预测人体关节角度。然而，这些方法通常面临诸如实时应用能力有限、测试条件代表性不足以及需要大量数据以达到最佳性能等问题。本文提出了一种转移学习框架，仅需新受试者几个步态周期的数据即可实现膝关节角度的预测。研究利用了三个数据集——佐治亚理工学院（Georgia Tech）、加州大学欧文分校（UCI）和夏里夫机械实验室外骨骼（SMLE），这些数据集中包含了与膝关节运动相关的四个EMG通道。 研究人员开发了一个轻量级基于注意力机制的CNN-LSTM模型，并在佐治亚理工大学的数据集上进行了预训练，然后将其转移到UCI和SMLE数据集。对于异常受试者，在仅使用EMG输入的情况下，该模型分别实现了6.8% 和13.7% 的归一化均方绝对误差（NMAE）的一步预测和50步预测。当引入历史膝关节角度信息后，对正常受试者的一步预测和50步预测的NMAE分别降至了3.1% 和3.5%，而对于异常受试者则分别为2.8% 和7.5%。 进一步将模型适应于SMLE外骨骼，并加入EMG、运动学以及交互力输入后，该模型对于一步预测和50步预测的NMAE分别达到了1.09% 和3.1%，展示了其在短期和长期康复场景中的强大泛化能力和稳健性能。
URL
https://arxiv.org/abs/2510.13443
