Muscle activities underlying many motor behaviors can be generated by a small number of basic activation patterns with specific features shared across movement conditions. Such low-dimensionality suggests that the central nervous system (CNS) relies on a modular organization to simplify control. However, the relationship between the dimensionality of muscle patterns and that of joint torques is not fixed, because of redundancy and non-linearity in mapping the former into the latter, and needs to be investigated. We compared the torques acting at four arm joints during fast reaching movements in different directions in the frontal and sagittal planes and the underlying muscle patterns. The dimensionality of the non-gravitational components of torques and muscle patterns in the spatial, temporal, and spatiotemporal domains was estimated by multidimensional decomposition techniques. The spatial organization of torques was captured by two or three generators, indicating that not all the available coordination patterns are employed by the CNS. A single temporal generator with a biphasic profile was identified, generalizing previous observations on a single plane. The number of spatiotemporal generators was equal to the product of the spatial and temporal dimensionalities and their organization was essentially synchronous. Muscle pattern dimensionalities were higher than torques dimensionalities but also higher than the minimum imposed by the inherent non-negativity of muscle activations. The spatiotemporal dimensionality of the muscle patterns was lower than the product of their spatial and temporal dimensionality, indicating the existence of specific asynchronous coordination patterns. Thus, the larger dimensionalities of the muscle patterns may be required for CNS to overcome the non-linearities of the musculoskeletal system and to flexibly generate endpoint trajectories with simple kinematic features using a limited number of building blocks.

The ability to intercept or avoid a moving object, whether to catch a ball, snatch one’s prey, or avoid the path of a predator, is a skill that has been acquired throughout evolution by many species in the animal kingdom. This requires processing early visual cues in order to program anticipatory motor responses tuned to the forthcoming event. Here, we explore the nature of the early kinematics cues that could inform an observer about the future direction of a ball projected with an unconstrained overarm throw. Our goal was to pinpoint the body segments that, throughout the temporal course of the throwing action, could provide key cues for accurately predicting the side of the outgoing ball. We recorded whole-body kinematics from twenty non-expert participants performing unconstrained overarm throws at four different targets placed on a vertical plane at 6 m distance. In order to characterize the spatiotemporal structure of the information embedded in the kinematics of the throwing action about the outgoing ball direction, we introduced a novel combination of dimensionality reduction and machine learning techniques. The recorded kinematics clearly shows that throwing styles differed considerably across individuals, with corresponding inter-individual differences in the spatio-temporal structure of the thrower predictability. We found that for most participants it is possible to predict the region where the ball hit the target plane, with an accuracy above 80%, as early as 400–500 ms before ball release. Interestingly, the body parts that provided the most informative cues about the action outcome varied with the throwing style and during the time course of the throwing action. Not surprisingly, at the very end of the action, the throwing arm is the most informative body segment. However, cues allowing for predictions to be made earlier than 200 ms before release are typically associated to other body parts, such as the lower limbs and the contralateral arm. These findings are discussed in the context of the sport-science literature on throwing and catching interactive tasks, as well as from the wider perspective of the role of sensorimotor coupling in interpersonal social interactions.

Overarm throwing is a fundamental human skill. Since paleolithic hunter-gatherer societies, the ability of throwing played a key role in brain and body co-evolution. For decades, throwing skill acquisition has been the subject of developmental and gender studies. However, due to its complex multijoint nature, whole body throwing has found little space in quantitative studies of motor behavior. In this study we examined how overarm throwing varies within and between individuals in a sample of untrained adults. To quantitatively compare whole body kinematics across throwing actions, we introduced a new combination of spatiotemporal principal component, linear discrimination, and clustering analyses. We found that the identity and gender of a thrower can be robustly inferred by the kinematics of a single throw, reflecting the characteristic features in individual throwing strategies and providing a quantitative ground for the well-known differences between males and females in throwing behavior. We also identified four main classes of throwing strategies, stable within individuals and resembling the main stages of throwing proficiency acquisition during motor development. These results support earlier proposals linking interindividual and gender differences in throwing, with skill acquisition interrupted at different stages of the typical developmental trajectory of throwing motor behavior. NEW & NOTEWORTHY Unconstrained throwing, because of its complexity, received little attention in quantitative motor control studies. By introducing a new approach to analyze whole body kinematics, we quantitatively characterized gender effects, interindividual differences, and common patterns in nontrained throwers. The four throwing styles identified across individuals resemble different stages in the acquisition of throwing skills during development. These results advance our understanding of complex motor skills, bridging the gap between motor control, motor development, and sport science.

Human dexterity far exceeds that of modern robots, despite a much slower neuromuscular system. Understanding how this is accomplished may lead to improved robot control. The slow neuromuscular system of humans implies that prediction based on some form of internal model plays a prominent role. However, the nature of the model itself remains unclear. To address this problem, we focused on one of the most complex and exotic tools humans can manipulate-a whip. We tested (in simulation) whether a distant target could be reached with a whip using a (small) number of dynamic primitives, whose parameters could be learned through optimization. This approach was able to manage the complexity of an (extremely) high degree-of-freedom system and discovered the optimal parameters of the upper-limb movement that achieved the task. A detailed model of the whip dynamics was not needed for this approach, which thereby significantly relieved the computational burden of task representation and performance optimization. These results support our hypothesis that composing control using dynamic motor primitives may be a strategy which humans use to enable their remarkable dexterity. A similar approach may contribute to improved robot control.

Background Maintaining upright posture is an unstable task that requires sophisticated neuro-muscular control. Humans use foot–ground interaction forces, characterized by point of application, magnitude, and direction to manage body accelerations. When analyzing the directions of the ground reaction forces of standing humans in the frequency domain, previous work found a consistent pattern in different frequency bands. To test whether this frequency-dependent behavior provided a distinctive signature of neural control or was a necessary consequence of biomechanics, this study simulated quiet standing and compared the results with human subject data. Methods Aiming to develop the simplest competent and neuromechanically justifiable dynamic model that could account for the pattern observed across multiple subjects, we first explored the minimum number of degrees of freedom required for the model. Then, we applied a well-established optimal control method that was parameterized to maximize physiologically-relevant insight to stabilize the balancing model. Results If a standing human was modeled as a single inverted pendulum, no controller could reproduce the experimentally observed pattern. The simplest competent model that approximated a standing human was a double inverted pendulum with torque-actuated ankle and hip joints. A range of controller parameters could stabilize this model and reproduce the general trend observed in experimental data; this result seems to indicate a biomechanical constraint and not a consequence of control. However, details of the frequency-dependent pattern varied substantially across tested control parameter values. The set of parameters that best reproduced the human experimental results suggests that the control strategy employed by human subjects to maintain quiet standing was best described by minimal control effort with an emphasis on ankle torque. Conclusions The findings suggest that the frequency-dependent pattern of ground reaction forces observed in quiet standing conveys quantitative information about human control strategies. This study’s method might be extended to investigate human neural control strategies in different contexts of balance, such as with an assistive device or in neurologically impaired subjects.