Surface Electromyography (sEMG) is a non-invasive signal that is used in the
recognition of hand movement patterns, the diagnosis of diseases, and the
robust control of prostheses. Despite the remarkable success of recent
end-to-end Deep Learning approaches, they are still limited by the need for
large amounts of labeled data. To alleviate the requirement for big data,
researchers utilize Feature Engineering, which involves decomposing the sEMG
signal into several spatial, temporal, and frequency features. In this paper,
we propose utilizing a feature-imitating network (FIN) for closed-form temporal
feature learning over a 300ms signal window on Ninapro DB2, and applying it to
the task of 17 hand movement recognition. We implement a lightweight LSTM-FIN
network to imitate four standard temporal features (entropy, root mean square,
variance, simple square integral). We then explore transfer learning
capabilities by applying the pre-trained LSTM-FIN for tuning to a downstream
hand movement recognition task. We observed that the LSTM network can achieve
up to 99\% R2 accuracy in feature reconstruction and 80\% accuracy in hand
movement recognition. Our results also showed that the model can be robustly
applied for both within- and cross-subject movement recognition, as well as
simulated low-latency environments. Overall, our work demonstrates the
potential of the FIN modeling paradigm in data-scarce scenarios for sEMG signal
processing.

通过使 sEMG 信号的终止形式获得闭式时间特征学习，我们实现了一个轻量级的 LSTM-FIN 网络，以模仿四个标准的时间特征（熵、均方根、方差、简单平方积分），并探索了迁移学习的能力。我们的结果表明，LSTM 网络在特征重构方面可以达到 99% 的 R2 准确度，在手部动作识别方面可以达到 80% 的准确度，而且在主体内和主体间的动作识别以及模拟的低延迟环境中能够具备稳健的应用能力。总体而言，我们的工作展示了在 sEMG 信号处理的数据缺乏情况下，FIN 建模范式的潜力。