# EMG-Driven Shared Control Architecture for Human-Robot Co-Manipulation Tasks +
## 5 Conclusions  
In this work, a shared control architecture is proposed that allows changing the admittance parameters of a manipulator in function of the EMG sensor readings in a human-robot co-manipulation scenario. A dedicated classifier recognizes the movements and contractions that the human operator performs, and this information is used to switch among three sets of admittance parameters to enable three different robot behaviors useful for performing several kinds of industrial applications. The architecture is validated on a Jaco2 robotic platform, making use of Trigno Avanti EMG sensors.  
The approach has been validated on a limited group of four participants, supporting its feasibility in a controlled setting. The validation on multiple users confirms the robustness and user-friendliness of the architecture. The significance of this research is most evident in its direct practical value for real-world applications. By allowing the operator to modulate the robot's behavior without interrupting the physical workflow, the system significantly enhances ergonomics and reduces cognitive load. The versatility offered by the defined no-contact, contact, and hold modes makes the system adaptable to a wide range of industrial scenarios, increasing the practical utility of collaborative robots.  
Future works will be devoted to the exploitation of the IMUs (Inertial Measurement Units) included in the EMG sensors, merging this information with the muscle signals to be capable of classifying more complex human motions. Additionally, methods for anticipating and predicting human motions will also be investigated in order to make the robot able to proactively change its behavior depending on the human operator's intentions, and the continuous time variation in the admittance parameters will be investigated for different scenarios and applications. Finally, efforts will also be devoted to exploring user-independent classifiers and learning techniques to extend the system's generalization beyond user-specific training. This includes the integration of advanced cross-user learning models to drastically reduce or eliminate the calibration time for new operators [43; 44].  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/machines13080669/s1](https://www.mdpi.com/article/10.3390/machines13080669/s1)]([https://www.mdpi.com/article/10.3390/machines13080669/s1](https://www.mdpi.com/article/10.3390/machines13080669/s1)), Video of the experiment performed by subject S1.  
Conceptualization, F.A.; methodology, F.A. and P.D.L.; software, F.P. and P.D.L.; validation, F.P. and P.D.L.; formal analysis, F.A.; investigation, F.A.; resources, F.A.; data curation, F.P.; writing--original draft preparation, F.P. and P.D.L.; writing--review and editing, F.A. and P.D.L.; visualization, F.P. and P.D.L.; supervision, F.A.; project administration, F.A.; funding acquisition, F.A. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from Project \"COM3: Cooperative Mobile Manipulators for Manufacturing\" CUP: H53D23000610006, \"COMET: Composable autonomy in Marine Environments\" CUP: 2022 TPSX25 and \"MAXFISH: Multi agents systems and Max-Plus algebra theoretical frameworks for a robot-fish shoal modelling and control\" CUP: 20225 RYME, funded by EU in NextGenerationEU plan through the Italian \"Bando Prin 2022-D.D. 104 del 02-02-2022\" by MUR.  
The research protocol was approved by the Research Ethics Committee of the University of Cassino and Southern Lazio.  
In accordance with the Declaration of Helsinki, all the participants provided signed informed consent.  
The original contributions presented in this study are included in the article and supplementary material. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.