Anthony M. Jarc

The case for and against muscle synergies

A 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.

Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dynamics

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 low-dimensional 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 systems 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.

FES Control of Isometric Forces in the Rat Hindlimb Using Many Muscles

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.

Robot-assisted surgery: an emerging platform for human neuroscience research.

Classic studies in human sensorimotor control use simplified tasks to uncover fundamental control strategies employed by the nervous system. Such simple tasks are critical for isolating specific features of motor, sensory, or cognitive processes, and for inferring causality between these features and observed behavioral changes. However, it remains unclear how these theories translate to complex sensorimotor tasks or to natural behaviors. Part of the difficulty in performing such experiments has been the lack of appropriate tools for measuring complex motor skills in real-world contexts. Robot-assisted surgery (RAS) provides an opportunity to overcome these challenges by enabling unobtrusive measurements of user behavior. In addition, a continuum of tasks with varying complexity—from simple tasks such as those in classic studies to highly complex tasks such as a surgical procedure—can be studied using RAS platforms. Finally, RAS includes a diverse participant population of inexperienced users all the way to expert surgeons. In this perspective, we illustrate how the characteristics of RAS systems make them compelling platforms to extend many theories in human neuroscience, as well as, to develop new theories altogether.

Development and Validation of Objective Performance Metrics for Robot-assisted Radical Prostatectomy – A Pilot Study

Purpose We explore and validate objective surgeon performance metrics using a novel recorder (“dVLogger”) to directly capture surgeon manipulations on the da Vinci® Surgical System. We present the initial construct and concurrent validation study of objective metrics during preselected steps of robot‐assisted radical prostatectomy. Materials and Methods Kinematic and events data were recorded for expert (100 or more cases) and novice (less than 100 cases) surgeons performing bladder mobilization, seminal vesicle dissection, anterior vesicourethral anastomosis and right pelvic lymphadenectomy. Expert/novice metrics were compared using mixed effect statistical modeling (construct validation). Expert reviewers blindly rated seminal vesicle dissection and anterior vesicourethral anastomosis using GEARS (Global Evaluative Assessment of Robotic Skills). Intraclass correlation measured inter‐rater variability. Objective metrics were correlated to corresponding GEARS metrics using Spearman’s test (concurrent validation). Results The performance of 10 experts (mean 810 cases, range 100 to 2,000) and 10 novices (mean 35 cases, range 5 to 80) was evaluated in 100 robot‐assisted radical prostatectomy cases. For construct validation the experts completed operative steps faster (p <0.001) with less instrument travel distance (p <0.01), less aggregate instrument idle time (p <0.001), shorter camera path length (p <0.001) and more frequent camera movements (p <0.03). Experts had a greater ratio of dominant‐to‐nondominant instrument path distance for all steps (p <0.04) except anterior vesicourethral anastomosis. For concurrent validation the median experience of 3 expert reviewers was 300 cases (range 200 to 500). Intraclass correlation among reviewers was 0.6‐0.7. For anterior vesicourethral anastomosis and seminal vesicle dissection, kinematic metrics had low associations with GEARS metrics. Conclusions 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.

Temporal clustering of surgical activities in robot-assisted surgery

PurposeMost evaluations of surgical workflow or surgeon skill use simple, descriptive statistics (e.g., time) across whole procedures, thereby deemphasizing critical steps and potentially obscuring critical inefficiencies or skill deficiencies. In this work, we examine off-line, temporal clustering methods that chunk training procedures into clinically relevant surgical tasks or steps during robot-assisted surgery.MethodsWe collected system kinematics and events data from nine surgeons performing five different surgical tasks on a porcine model using the da Vinci Si surgical system. The five tasks were treated as one ‘pseudo-procedure.’ We compared four different temporal clustering algorithms—hierarchical aligned cluster analysis (HACA), aligned cluster analysis (ACA), spectral clustering (SC), and Gaussian mixture model (GMM)—using multiple feature sets.ResultsHACA outperformed the other methods reaching an average segmentation accuracy of

Training in divergent and convergent force fields during 6-DOF teleoperation with a robot-assisted surgical system

88.0\%

PD41-12 THE QUANTIFIED SURGEON: DEFINING AND VALIDATING CLINICAL PERFORMANCE METRICS DURING ROBOTIC RADICAL PROSTATECTOMY

88.0% when using all system kinematics and events data as features. SC and ACA reached moderate performance with