This paper investigates hand grasping, a fundamental activity in daily living, by examining the forces and postures involved in the lift-and-hold phases of grasping. We introduce a novel multi-sensory data glove, integrated with resistive flex sensors and capacitive force sensors, to measure the intricate dynamics of hand movement. The study engaged five subjects to capture a comprehensive dataset that includes contact forces at the fingertips and joint angles, furnishing a detailed portrayal of grasp mechanics. Focusing on grasp synergies, our analysis delved into the quantitative relationships between the correlated forces among the fingers. By manipulating one variable at a time—either the object or the subject—our cross-sectional approach yields rich insights into the nature of grasp forces and angles. The correlation coefficients for finger pairs presented median values ranging from 0.5 to nearly 0.9, indicating varying degrees of inter-finger coordination, with the thumb-index and index-middle pairs exhibiting particularly high synergy. The findings, depicted through spider charts and correlation coefficients, reveal significant patterns of cooperative finger behavior. These insights are crucial for the advancement of hand mechanics understanding and have profound implications for the development of assistive technologies and rehabilitation devices.

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.