标题： EMGTTL：基于Transformer的迁移学习用于使用原始表面肌电信号分类ADL 显示英文标题

标题： EMGTTL: Transformers-Based Transfer Learning for Classification of ADL using Raw Surface EMG Signals

摘要： 表面肌电图（sEMG）广泛应用于康复、假肢、机器人手臂控制和人机交互等领域。然而，使用sEMG信号分类日常生活活动（ADL）通常需要大量的特征提取，这可能耗时且能耗高。 本研究的目标如下： 给定sEMG数据集，如人类活动数据库（DB1和DB4）的肌电图分析，其中多通道信号对应于ADL，是否有可能在不对sEMG信号进行显式特征提取的情况下确定ADL类别。进一步地，是否有可能通过跨数据集学习来提高分类性能。 开发了一种名为EMGTTL的分类框架，该框架使用变换器对ADL进行分类，并通过跨数据传递学习增强了性能。该方法在EMAHA-DB1和EMAHA-DB4上实现。 实验表明，对于DB1，变换器架构达到了64.47%的准确率，对于DB4，达到了68.82%的准确率。此外，使用迁移学习后，DB1的准确率提升至66.75%（在DB4上预训练），DB4的准确率提升至71.04%（在DB1上预训练）。 显示英文摘要

摘要： Surface Electromyography (sEMG) is widely studied for its applications in rehabilitation, prosthetics, robotic arm control, and human-machine interaction. However, classifying Activities of Daily Living (ADL) using sEMG signals often requires extensive feature extraction, which can be time-consuming and energy-intensive. The objective of this study is stated as follows. Given sEMG datasets, such as electromyography analysis of human activity databases (DB1 and DB4), with multi-channel signals corresponding to ADL, is it possible to determine the ADL categories without explicit feature extraction from sEMG signals. Further is it possible to learn across the datasets to improve the classification performances. A classification framework, named EMGTTL, is developed that uses transformers for classification of ADL and the performance is enhanced by cross-data transfer learning. The methodology is implemented on EMAHA-DB1 and EMAHA-DB4. Experiments have shown that the transformer architecture achieved 64.47% accuracy for DB1 and 68.82% for DB4. Further, using transfer learning, the accuracy improved to 66.75% for DB1 (pre-trained on DB4) and 71.04% for DB4 (pre-trained on DB1).