Lin Chuang, Zhao Chunxiao, Zhang Jianhua, Chen Chen, Jiang Ning, Farina Dario, Guo Weiyu
IEEE Trans Neural Syst Rehabil Eng. 2024 Dec 10;PP. doi: 10.1109/TNSRE.2024.3514938.
Surface Electromyographic (sEMG) signals contain motor-related information and therefore can be used for human-machine interaction (HMI). Deep learning plays an important role in extracting motor-related information from sEMG signals. However, most studies prioritize model accuracy without sufficient consideration of model efficiency, including the model size, power consumption, and the computational speed of the model. This leads to impractical power consumption, heat dissipation levels and processing time in wearable computation scenarios. Here, we propose an efficient Transformer method that employs the EMSA (Efficient Multiple Self-Attention) and pruning mechanism to improve efficiency and accuracy concurrently, when estimating finger joint angles from sEMG signals. The proposed method does not only achieve state-of-the-art accuracy but can also be deployed on wearable devices to satisfy real-time applications. We applied the proposed model on the Ninapro DB2-dataset to estimate finger joint angles during grasping tasks. RNN series models, Convolution series models, and Transformer series models were used as reference models for comparison. In addition to common model accuracy, the deployment performance of the models was tested on microprocessors, such as Intel CPU i5, Apple M1, and Raspberry Pi 4B. When tested on 38 subjects of the Ninapro DB2, the proposed model resulted in a correlation coefficient of 0.82 ± 0.04, root mean squared error (RMSE) of 10.77 ± 1.48, and normalized RMSE of 0.11 ± 0.01, which were all similar to the results achieved by the state-of-the-art (SOTA) reference methods. Further, the computational time of the proposed methods was 65.99 ms on the Raspberry Pi 4B, which outperformed all the RNN series models and the Transformer series models. The model size and the power (the minimum size and power are 0.39 MB and 2.28 w) consumption of the proposed model also outperformed that of all reference Transformer methods. These experimental results indicate that our model can maintain the accuracy of the SOTA methods while significantly improving efficiency, thus being a promising approach for real-life applications in wearable devices.
表面肌电（sEMG）信号包含与运动相关的信息，因此可用于人机交互（HMI）。深度学习在从sEMG信号中提取与运动相关的信息方面发挥着重要作用。然而，大多数研究在没有充分考虑模型效率的情况下优先考虑模型准确性，包括模型大小、功耗和模型的计算速度。这导致在可穿戴计算场景中出现不切实际的功耗、散热水平和处理时间。在此，我们提出一种高效的Transformer方法，该方法采用高效多重自注意力（EMSA）和剪枝机制，在从sEMG信号估计手指关节角度时同时提高效率和准确性。所提出的方法不仅实现了最先进的准确性，还可以部署在可穿戴设备上以满足实时应用。我们将所提出的模型应用于Ninapro DB2数据集，以估计抓握任务期间的手指关节角度。循环神经网络（RNN）系列模型、卷积系列模型和Transformer系列模型用作参考模型进行比较。除了常见的模型准确性外，还在诸如英特尔酷睿i5、苹果M1和树莓派4B等微处理器上测试了模型的部署性能。在Ninapro DB2的38名受试者上进行测试时，所提出的模型的相关系数为0.82±0.04，均方根误差（RMSE）为10.77±1.48，归一化RMSE为0.11±0.01，这些结果均与最先进（SOTA）参考方法所取得的结果相似。此外，所提出方法在树莓派4B上的计算时间为65.99毫秒，优于所有RNN系列模型和Transformer系列模型。所提出模型的模型大小和功耗（最小大小和功耗分别为0.39MB和2.28瓦）也优于所有参考Transformer方法。这些实验结果表明，我们的模型可以在保持SOTA方法准确性的同时显著提高效率，因此是可穿戴设备实际应用中的一种有前途的方法。
