## 2 Methods
### DAIDA Framework
#### 2.2.3 DAIDA Framework  
The DAIDA framework we used, shown in Figure 4, has a similar structure to the baseline model, sharing the same convolutional module for feature extraction. The model then generates branches, one of which is a labeled classifier for the motion classification trained in the source and target domains. The second branch is a domain classifier using a Gradient Reversal Layer (GRL), which is set between the domain classifier and the feature extractor to determine whether the collected feature distributions are from the source or target domain. The GRL layer that maximizes the loss of the classifier forces the feature extraction block to learn features that are not related to the domain, i.e., domain-invariant features. Ultimately, the model learns features with better generalization performance. This operation takes place during the backpropagation phase and consists of multiplying the  
Figure 3: Overview of the baseline model. (**a**) is the macroscopic diagram of the model. (**b**) is the architecture of the Stem Block. (**c**) is the architecture of the Inception Block. (**d**) shows the architectures of Inception A, the Inception B and the Inception C module, which are used to replace the Inception Block in (**c**), respectively.  
gradient by a negative value (\(-\lambda\)) while, during forward propagation, the gradient acts as an identity transform. The GRL can be easily implemented to any feed-forward network without any additional parameters.  
Consider a classification problem with the input feature vector space X and Y = {1, 2, 3, 4, 5, 6} as the set of labels in the output space. Let {Xs, Ys} and {Xt, Yt} be unknown joint distributions defined over X \(\times\) Y, referred to as the source and target distributions, respectively. The unsupervised domain adaptation algorithm requires input as the labeled source domain data, sampled from {Xs, Ys}, and unlabeled target domain data, sampled from the marginal distribution {Xt}. The data from the source and target domains are fed into the feature extractor, and the features are classified by the domain classifier.  
The architecture has two networks, and each network consists of a feature extractor and a classifier. The feature extractor \(G_{f}\), shown in Figure 4, is a parallel convolution network and takes the preprocessed sEMG input vector \(x_{i}\) and generates a feature vector \(f_{i}\), i.e.,  
\[f_{i}=G_{f}\Big{(}x_{i};\,\theta_{f}\Big{)} \tag{1}\]  
The feature vector \(f_{i}\) extracted from \(G_{f}\) is mapped to a class label by the label classifier \(G_{y}\) and to a domain label by the domain classifier \(G_{d}\), i.e.,  
\[y_{i}=G_{y}\big{(}f_{i};\,\theta_{y}\big{)} \tag{2}\]  
\[d_{i}=G_{d}(f_{i};\,\theta_{d}) \tag{3}\]  
Both the label classifier and the domain classifier are multilayer feed-forward neural networks whose parameters are referred to as \(\theta_{y}\) and \(\theta_{d}\), respectively. During training, the goal of the feature extractor is to minimize the classification error while making the domain classifier unable to accurately distinguish between the feature representations of the source and target domains. In this way, the feature extractor is able to learn feature representations that have good generalization ability to both the source and target domains.  
In the proposed DAIDA scheme, we utilize parallel convolution to extract effective motion features, and employ adversarial training to simultaneously minimize the classification loss and maximize the diversity of domain distribution. This enables us to eventually achieve a position-invariant model. The complete DAIDA algorithm is described in Algorithm 1.