The human hand is an intricate anatomical structure essential for daily activities, yet replicating its full functionality in upper-limb prostheses remains a significant challenge. Despite advances in mechanical design leading to more sophisticated and dexterous artificial hands, difficulties persist in effectively controlling these prostheses due to the limitations posed by the muscle conditions of their users. These constraints result in a limited number of control inputs and a lack of sensory feedback. To address these issues, various semi-autonomous control strategies have been proposed, which integrate sensing technologies to complement traditional myoelectric control. Inspired by human grasping physiology, we propose a shared control strategy that divides grasp control into two levels: a high-level controller, operated by the user to initiate the grasp action, and a low-level controller, which ensures stability throughout the task. This work focuses specifically on slip detection methods, introducing improvements to the low-level controller to enable more autonomous grasping behavior during object holding. The proposed slip module uses distributed 3D force sensors across the artificial hand and integrates a friction cone strategy to ensure an appropriate shear-to-normal force ratio with bandpass filtering for establishing an initial stable grasp model without prior knowledge. Experimental evaluations consist of the comparison of this novel controller with conventional state-of-the-art approaches. Results demonstrate its efficacy in preventing slippage while requiring less grasping force than previous methods. Additionally, a qualitative validation was conducted to assess its responsiveness compared to human grasping reactions to unexpected weight changes, yielding positive outcomes.

Advanced myoelectric prostheses feature multiple degrees of freedom (DoFs) and sophisticated control algorithms that interpret user motor intentions as commands. While enhancing their capability to assist users in a wide range of daily activities, these control solutions still pose challenges. Among them, the need for extensive learning periods and users’ limited control proficiency. To investigate the relationship between these challenges and the limited alignment of such methods with human motor control strategies, we examine motor learning processes in two different control maps testing a synergistic myoelectric system. In particular, this work employs a DoF-wise synergies control algorithm tested in both intuitive and non-intuitive control mappings. Intuitive mapping aligns body movements with control actions to replicate natural limb control, whereas non-intuitive mapping (or non-biomimetic) lacks a direct correlation between aspects, allowing one body movement to influence multiple DoFs. The latter offers increased design flexibility through redundancy, which can be especially advantageous for individuals with motor disabilities. The study evaluates the effectiveness and learning process of both control mappings with 10 able-bodied participants. The results revealed distinct patterns observed while testing the two maps. Furthermore, muscle synergies exhibited greater stability and distinction by the end of the experiment, indicative of varied learning processes.