A kinematic-muscular synergy-based framework to evaluate user transparency and assistance in human-exoskeleton interaction

Exoskeletons are becoming increasingly popular solutions to promote motor recovery in patients with neurological disorders in rehabilitation. In the framework of human-device interaction, perceived user transparency plays a crucial role, as it allows users to move naturally and remain actively engaged during task execution. In this context, synergy-based approaches offer a valuable tool to gain a deeper understanding of the neural mechanisms underlying transparency and robot assistance. This study investigated the user transparency of the ALEx upper-limb exoskeleton and the effects​ of robotic assistance by means of the novel kinematic-muscular synergies, which describe how muscle synergies connect to the task space. Sixteen healthy subjects were recruited to perform a wide set of standardized arm movements in three modalities ( Free , i.e., free movement, Transparent , i.e., robot provides no assistance, and Assisted , i.e., robot assists motion). Fifteen electromyographic (EMG) upper-limb and trunk muscles and trunk and upper-limb kinematics were collected to allow the extraction of kinematic-muscular synergies through the Mixed Matrix Factorization (MMF) algorithm. Averaged matched synergies were compared across modalities in terms of their spatial weights and temporal components to examine how the interaction with the exoskeleton altered the motor modules, providing a neurophysiological indicator of transparency. Six synergies were extracted from each modality to allow direct comparisons across conditions. The spatial and temporal structure of the synergies typical of Free movement were largely preserved when interacting with the device in the Transparent modality (similarities > 0.85 spatial, > 0.75 temporal), and in the Assisted (similarities > 0.90 spatial, > 0.70 temporal), even though a decrease in temporal magnitude ( p = 0.004) was registered during the Assisted modality, indicating a reduction of the muscular effort. Our findings suggest that ALEx achieves a good level of user transparency and reproduces physiological motor patterns in the Assisted modality. The proposed MMF-based approach offers a promising tool for evaluating the impact of robotic devices on motor control, by explicating the link between the neural space, typically analysed in synergies, and the corresponding task performance.