The coordination of the multiple degrees-of-freedom of the human body may be simplified by muscle synergies, motor modules which can be flexibly combined to achieve various goals. Studies investigating adaptation to novel relationships between muscle activity and task outcomes found that altering the recruitment of such modules is faster than the learning of their structures de novo. However, how learning new synergy recruitments or new synergy structures may occur remains unclear. While trial-by-trial learning of novel sensorimotor tasks has been successfully modeled at the level of task variables, few models accounted for the redundancy of the motor system, particularly at the muscular level. However, these models either did not consider a modular architecture of the motor system, or assumed a priori knowledge of the sensorimotor task. Here, we present a computational model for the generation of redundant muscle activity where explicitly defined modules, implemented as spatial muscle synergies, can be updated together with their recruitment coefficients through an error-based learning process dependent on a forward model of the sensorimotor task, which is not assumed to be known a priori. Our model can qualitatively reproduce the experimental observations of slower learning and larger changes in the structure of the muscle activity under sensorimotor tasks that require the learning of novel patterns of muscle activity, providing further insights into the modular organization of the human motor system.

This article presents the results of an interdisciplinary study encompassing data collected from 108 in-depth interviews and 15 participatory workshops aimed at exploring the requirements of a diverse range of stakeholders in relation to assistive augmentative technologies. The study involved 39 post-stroke patients with hand paresis, 39 individuals with spinal cord injuries affecting upper limb function, 38 relatives and 65 healthcare professionals. Through qualitative analysis of the accumulated data, this research identified the multifaceted needs, preferences and aspirations of the study participants. We propose a structured hierarchy for user requirements, inspired by Maslow's hierarchy of needs. At the foundation of this pyramid are the essential functional requirements, forming the bedrock for the subsequent layers. Moving hierarchically, we identified reliability and security requirements, followed by usability and user experience considerations, recognition requirements and, finally, personal growth and self-fulfilment criteria. The foundational functional requirements must be fulfilled before other aspects can take precedence, creating a systematic approach to address the diverse needs of individuals with upper limb disabilities. The pyramid of requirements identified may significantly contribute to the scholarly community and lay the foundation for the development of assistive robotics that align with the desires of individuals with upper limb disabilities.

Supernumerary robotic limbs (SRLs) gained increasing interest in the last years for their applicability as healthcare and assistive technologies. These devices can either support or augment human sensorimotor capabilities, allowing users to complete tasks that are more complex than those feasible for their natural limbs. However, for a successful coordination between natural and artificial limbs, intuitiveness of interaction and perception of autonomy are key enabling features, especially for people suffering from motor disorders and impairments. The development of suitable human-robot interfaces is thus fundamental to foster the adoption of SRLs. With this work, we describe how to control an extra degree of freedom by taking advantage of what we defined the Intrinsic Kinematic Null Space, i.e. the redundancy of the human kinematic chain involved in the ongoing task. Obtained results demonstrated that the proposed control strategy is effective for performing complex tasks with a supernumerary robotic finger, and that practice improves users' control ability.

The question of whether the nervous system produces movement through the combination of a few discrete elements has long been central to the study of motor control. Muscle synergies, i.e. coordinated patterns of muscle activity, have been proposed as possible building blocks. Here we propose a model based on combinations of muscle synergies with a spe- cific amplitude and temporal structure. Time-varying synergies provide a realistic basis for the decomposition of the complex patterns observed in natural behaviors. To extract time-varying synergies from simultane- ous recording of EMG activity we developed an algorithm which extends existing non-negative matrix factorization techniques.