# EMG-Driven Shared Control Architecture for Human-Robot Co-Manipulation Tasks +
## 4 Discussion and Limitations  
This section presents an analysis of the experimental results and critically examines the current limitations of the proposed shared control architecture, while also outlining directions for future development. The architecture presented in this work has been experimentally validated on a limited group of four participants, showing encouraging results in terms of both classifier performance and user experience. The Support Vector Machine (SVM) classifier achieved solid classification accuracy across all subjects, with F1-scores ranging from approximately 78% to 91%. This confirms that the use of EMG-based muscle contractions can effectively enable intuitive control mode switching during human-robot physical interaction. Moreover, the experiments demonstrated that the finite state machine (FSM) transitions were reliably triggered by the classifier outputs, enabling seamless switches between operational modes in real-time. Nonetheless, several aspects warrant further discussion. The user study was conducted with only four individuals, including one expert and three novices, which limits the statistical robustness and generalizability of the results. While the proposed interface performed well across all participants, broader validation across a more diverse and larger user population will be necessary to draw definitive conclusions regarding its applicability in real-world collaborative settings. Another important limitation lies in the training procedure. The classifier was trained individually for each user through a personalized acquisition session. While this guarantees high classification accuracy, it limits scalability and may be impractical in industrial contexts where fast setup and user independence are highly desirable. Future efforts will focus on implementing domain adaptation techniques or user-independent learning strategies to enhance the generality of the approach. The goal is to develop cross-user models that can generalize to new operators with little or no user-specific training data. Figure 7: Results of one of the experiments of the Subject 1: (**top**) EMG classifier output, i.e., the statistical mode of the last 1.5 s of predicted classes; (**middle**) force measured by the F/T sensor; (**bottom**) end-effector linear velocity. The different colored areas on all graphs correspond to the three different control modes: green, blue and red represent the _no-contact_, _contact_, and _hold_ modes, respectively. Significant progress is being made in this area through the application of advanced machine learning, including deep learning frameworks that can learn user-invariant feature representations [43] and novel architectures like graph convolutional networks designed to model spatial-temporal EMG patterns across subjects [44]. However, the framework developed in this work is inherently modular and could be easily enhanced by incorporating the aforementioned techniques. The set of EMG classes used in this work is intentionally limited to enable robust switching between three predefined operational modes. While sufficient for the demonstrated use case, this limited set restricts the complexity of tasks that can be performed. The extension to additional contraction classes, possibly combined with motion information from embedded inertial sensors, could allow proper control in more dynamic environments. Additionally, although detailed latency measurements were not collected, the overall system demonstrated real-time responsiveness. The 1.5 s windowed classifier mode output and FSM transitions showed minimal perceptible delay to users. Timing aspects such as classifier inference time, FSM transition delay, and controller actuation latency were not systematically measured. Such evaluation plays an important role in industrial deployment and will be prioritized in future studies. Finally, it is important to highlight that the control strategy and classifier design are, in principle, system-independent.