SummaryA long standing goal in motor control is to determine the fundamental output controlled by the CNS: does the CNS control the activation of individual motor units, individual muscles, groups of muscles, kinematic or dynamic features of movement, or does it simply care about accomplishing a task? Of course, the output controlled by the CNS might not be exclusive but instead multiple outputs might be controlled in parallel or hierarchically. In this review we examine one particular hypothesized level of control: that the CNS produces movement through the flexible combination of groups of muscles, or muscle synergies. Several recent studies have examined this hypothesis, providing evidence both in support and in opposition to it. We discuss these results and the current state of the muscle synergy hypothesis.
The basic hypothesis of producing a range of behaviors using a small set of motor commands has been proposed in various forms to explain motor behaviors ranging from basic reflexes to complex voluntary movements. Yet many fundamental questions regarding this long-standing hypothesis remain unanswered. Indeed, given the prominent nonlinearities and high dimensionality inherent in the control of biological limbs, the basic feasibility of a lowdimensional controller and an underlying principle for its creation has remained elusive. We propose a principle for the design of such a controller, that it endeavors to control the natural dynamics of the limb, taking into account the nature of the task being performed. Using this principle, we obtained a low-dimensional model of the hindlimb and a set of muscle synergies to command it. We demonstrate that this set of synergies was capable of producing effective control, establishing the viability of this muscle synergy hypothesis. Finally, by combining the low-dimensional model and the muscle synergies we were able to build a relatively simple controller whose overall performance was close to that of the system's full-dimensional nonlinear controller. Taken together, the results of this study establish that a low-dimensional controller is capable of simplifying control without degrading performance.low-dimensional ͉ optimal control ͉ muscle pattern ͉ frog ͉ computational model C ontrolling any movement, whether it be a stereotyped reflex or a sophisticated skill, is highly complex. Fundamentally, every movement requires the detailed specification of a vast number of variables, potentially involving many thousands of motor units distributed throughout the limbs and body. Further, the relationship between these variables and the intended motion of the body is nontrivial, dictated by the intricate nonlinear dynamics of the musculoskeletal system. Elucidating control strategies that can overcome these complexities is a central issue in the neural control of movement.Many investigators have suggested that the central nervous system (CNS) might have developed strategies to simplify the control of movement (1-6). According to one common proposal, the CNS might produce movement through the flexible combination of ''muscle synergies,'' with each such synergy specifying a particular balance of activation across a set of muscles (7-16). By reducing the number of controlled variables, such a lowdimensional control strategy would simplify the production of movement.Although many experiments have found evidence to suggest that many behaviors can be produced through combinations of muscle synergies, several questions concerning this hypothesis remain unresolved. Foremost among these questions is a proof of the concept's viability: can a low-dimensional control scheme based on muscle synergies reproduce the range of observed behaviors with negligible loss of efficacy? Given the nonlinearities and high dimensionality inherent in biological motor control, the answer to this question is not ...
To our knowledge, ours is the first study to show that APMs and ML algorithms may help assess surgical RARP performance and predict clinical outcomes. With further accrual of clinical data (oncologic and functional data), this process will become increasingly relevant and valuable in surgical assessment and training.
Objective metrics revealed experts to be more efficient and directed during preselected steps of robot-assisted radical prostatectomy. Objective metrics had limited associations to GEARS. These findings lay the foundation for developing standardized metrics for surgeon training and assessment.
ObjectivesTo evaluate automated performance metrics (APMs) and clinical data of experts and super-experts for four cardinal steps of robot-assisted radical prostatectomy (RARP): bladder neck dissection; pedicle dissection; prostate apex dissection; and vesico-urethral anastomosis. Subjects and MethodsWe captured APMs (motion tracking and system events data) and synchronized surgical video during RARP. APMs were compared between two experience levels: experts (100-750 cases) and super-experts (2100-3500 cases). Clinical outcomes (peri-operative, oncological and functional) were then compared between the two groups. APMs and outcomes were analysed for 125 RARPs using multi-level mixed-effect modelling. ResultsFor the four cardinal steps selected, super-experts showed differences in select APMs compared with experts (P < 0.05). Despite similar PSA and Gleason scores, super-experts outperformed experts clinically with regard to peri-operative outcomes, with a greater lymph node yield of 22.6 vs 14.9 nodes, respectively (P < 0.01), less blood loss (125 vs 130 mL, respectively; P < 0.01), and fewer readmissions at 30 days (1% vs 13%, respectively; P = 0.02). A similar but nonsignificant trend was seen for oncological and functional outcomes, with super-experts having a lower rate of biochemical recurrence compared with experts (5% vs 15%, respectively; P = 0.13) and a higher continence rate at 3 months (36% vs 18%, respectively; P = 0.14). ConclusionWe found that experts and super-experts differed significantly in select APMs for the four cardinal steps of RARP, indicating that surgeons do continue to improve in performance even after achieving expertise. We hope ultimately to identify associations between APMs and clinical outcomes to tailor interventions to surgeons and optimize patient outcomes.
Automated performance metrics can distinguish surgeon expertise during vesicourethral anastomosis. The expert vesicourethral anastomosis technique was associated with more efficient movement and less tissue trauma. Standardizing robotic vesicourethral anastomosis and using a methodically developed tutorial may help improve robotic surgical training.
Adverse surgical outcomes are costly to patients and hospitals. Approaches to benchmark surgical care are often limited to gross measures across the entire procedure despite the performance of particular tasks being largely responsible for undesirable outcomes. In order to produce metrics from tasks as opposed to the whole procedure, methods to recognize automatically individual surgical tasks are needed. In this paper, we propose several approaches to recognize surgical activities in robot-assisted minimally invasive surgery using deep learning. We collected a clinical dataset of 100 robot-assisted radical prostatectomies (RARP) with 12 tasks each and propose 'RP-Net', a modified version of InceptionV3 model, for image based surgical activity recognition. We achieve an average precision of 80.9% and average recall of 76.7% across all tasks using RP-Net which out-performs all other RNN and CNN based models explored in this paper. Our results suggest that automatic surgical activity recognition during RARP is feasible and can be the foundation for advanced analytics.
Functional electrical stimulation (FES) attempts to restore motor behaviors to paralyzed limbs by electrically stimulating nerves and/or muscles. This restoration of behavior requires specifying commands to a large number of muscles, each making an independent contribution to the ongoing behavior. Efforts to develop FES systems in humans have generally been limited to preprogrammed, fixed muscle activation patterns. The development and evaluation of more sophisticated FES control strategies is difficult to accomplish in humans, mainly because of the limited access of patients for FES experiments. Here, we developed an in vivo FES test platform using a rat model that is capable of using many muscles for control and that can therefore be used to evaluate potential strategies for developing flexible FES control strategies. We first validated this FES test platform by showing consistent force responses to repeated stimulation, monotonically increasing muscle recruitment with constant force directions, and linear summation of costimulated muscles. These results demonstrate that we are able to differentially control the activation of many muscles, despite the small size of the rat hindlimb. We then demonstrate the utility of this platform to test potential FES control strategies, using it to test our ability to effectively produce open-loop control of isometric forces. We show that we are able to use this preparation to produce a range of endpoint forces flexibly and with good accuracy. We suggest that this platform will aid in FES controller design, development, and evaluation, thus accelerating the development of effective FES applications for the restoration of movement in paralyzed patients.
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