Surface electromyography (sEMG) has demonstrated significant potential in simultaneous and proportional control (SPC). However, existing algorithms for predicting joint angles based on sEMG often suffer from high inference costs or are limited to specific subjects rather than cross-subject scenarios. To address these challenges, we introduced a hierarchical Spiking Attentional Feature Decomposition Network (SAFE-Net). This network initially compresses sEMG signals into neural spiking forms using a Spiking Sparse Attention Encoder (SSAE). Subsequently, the compressed features are decomposed into kinematic and biological features through a Spiking Attentional Feature Decomposition (SAFD) module. Finally, the kinematic and biological features are used to predict joint angles and identify subject identities, respectively. Our validation on two datasets (SIAT-DB1 and SIAT-DB2) and comparison with two existing methods, Informer and Spikformer, demonstrate that SSAE achieves significant power consumption savings of 39.1% and 37.5% respectively over them in terms of inference costs. Furthermore, SAFE-Net surpasses Informer and Spikformer in recognition accuracy on both datasets. This study underscores the potential of SAFE-Net to advance the field of SPC in lower limb rehabilitation exoskeleton robots.

中文翻译：

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通过分层尖峰注意特征分解网络进行高效的基于表面肌电图的跨受试者关节角度估计

表面肌电图 (sEMG) 已证明在同步和比例控制 (SPC) 方面具有巨大潜力。然而，现有的基于表面肌电图的关节角度预测算法往往推理成本较高，或者仅限于特定主题而不是跨主题场景。为了应对这些挑战，我们引入了分层尖峰注意力特征分解网络（SAFE-Net）。该网络最初使用尖峰稀疏注意力编码器（SSAE）将 sEMG 信号压缩为神经尖峰形式。随后，通过尖峰注意力特征分解（SAFD）模块将压缩特征分解为运动学和生物特征。最后，运动学和生物学特征分别用于预测关节角度和识别受试者身份。我们对两个数据集（SIAT-DB1 和 SIAT-DB2）的验证以及与两种现有方法 Informer 和 Spikformer 的比较表明，SSAE 在推理成本方面分别比它们节省了 39.1% 和 37.5% 的显着功耗。此外，SAFE-Net 在两个数据集上的识别准确性均超过了 Informer 和 Spikformer。这项研究强调了 SAFE-Net 在推进下肢康复外骨骼机器人 SPC 领域发展的潜力。