Uncontrolled manifold analysis of arm joint angle variability during robotic teleoperation and freehand movement of surgeons and novices

Experience influences kinematic motor synergies: an Uncontrolled manifold approach to simulated Nordic skiing

Motor synergies are defined as central nervous system mechanisms which adjust participating degrees of freedom to ensure dynamic stability (control) of certain performance variables and have been identified during many motor tasks. The potential for synergistic control of individual segments during full-body tasks is often overlooked. Thus, this study compared individual differences in the potential stabilization of multiple performance variables on the basis of experience during a full-body sport activity. Normalized time series of synergy indices from Uncontrolled Manifold analyses on experienced (n = 9) and inexperienced (n = 19) participants were analysed using statistical parametric mapping during simulated Nordic skiing. Regardless of experience, hand, upper arm, and whole-body centre of mass (COM) kinematics were found to be stabilized by kinematic motor synergies. Only experienced Nordic skiers stabilized trunk COM position at all, while trunk COM velocity was stabilized for a longer duration than inexperienced participants. However, inexperienced participants stabilized hand velocity for a greater duration overall and to a greater magnitude during early pull phase than the experienced skiers. That motor synergies for hand and trunk COM velocity differed between experience groups suggests potential utility for these performance variables as indicators of motor skill development for full-body tasks such as Nordic skiing.

Abnormal coordination of upper extremity during target reaching in persons post stroke

Understanding abnormal synergy of the upper extremity (UE) in stroke survivors is critical for better identification of motor impairment. Here, we investigated to what extent stroke survivors retain the ability to coordinate multiple joints of the arm during a reaching task. Using an exoskeleton robot, 37 stroke survivors' arm joint angles (θ) and torques (τ) during hand reaching in the horizontal plane was compared to that of 13 healthy controls. Kinematic and kinetic coordination patterns were quantified as variances of the multiple-joint angles and multiple-joint torques across trials, respectively, that were partitioned into task-irrelevant variance (TIVθ and TIVτ) and task-relevant variance (TRVθ and TRVτ). TIVθ and TRVθ (or TIVτ and TRVτ) led to consistent and inconsistent hand position (or force), respectively. The index of synergy (ISθ and ISτ) was determined as [Formula: see text] and [Formula: see text] for kinematic and kinetic coordination patterns, respectively. Both kinematic ISθ and kinetic ISτ in the stroke group were significantly lower than that of the control group, indicating stroke survivors had impaired reaching abilities in utilizing the multiple joints of the UE for successful completion of a reaching task. The reduction of kinematic ISθ in the stroke group was mainly attributed to the lower TIVθ as compared to the control group, while the reduction of kinetic ISτ was mainly due to the higher [Formula: see text] as well as lower TIVτ. Our results also indicated that stroke may lead to motor deficits in formation of abnormal kinetic synergistic movement of UE, especially during outward movement. The findings in abnormal synergy patterns provides a better understanding of motor impairment, suggesting that impairment-specific treatment could be identified to help improve UE synergies, focusing on outward movements.

The ergonomic impact of patient body mass index on surgeon posture during simulated laparoscopy

Laparoscopy is a cornerstone of modern surgical care, with clear advantages for the patients. However, it has also been associated with inducing upper body musculoskeletal disorders amongst surgeons due to their propensity to assume non-neutral postures. Further, there is a perception that patients with high body mass indexes (BMI) exacerbate these factors. Therefore, surgeon upper body postures were objectively quantified using inertial measurement units and the LUBA ergonomic framework was used to assess posture during laparoscopic training on patient models that simulated BMIs of 20, 30, 40 and 50 kg/m². In all surgeons the posture of the upper body significantly worsened during simulated laparoscopic surgery on the BMI 50 kg/m² model as compared to the baseline BMI model of 20 kg/m². These findings suggest that performing laparoscopic surgery on patients with high BMIs increases the prevalence of non-neutral posture and may further increase the risk of musculoskeletal disorders in surgeons.

Grasping Assisting Algorithm in Tele-Operated Robotic Gripper

The involvement of Robots and automated machines in different industries has increased drastically in recent years. Part of this revolution is accomplishing tasks previously performed by humans with advanced robots, which would replace the entire human workforce in the future. In some industries the workers are required to complete different operations in hazardous or difficult environments. Operations like these could be replaced with the use of tele-operated systems that have the capability of grasping objects in their surroundings, thus abandoning the need for the physical presence of the human operator at the area while still allowing control. In this research our goal is to create an assisting system that would improve the grasping of a human operator using a tele-operated robotic gripper and arm, while advising the operator but not forcing a solution. For a given set of objects we computed the optimal grasp to be achieved by the gripper, based on two grasp quality measures of our choosing (namely power grasp and precision grasp). We then tested the performance of different human subjects who tried to grasp the different objects with the tele-operated system, while comparing their success to unassisted and assisted grasping. Our goal is to create an assisting algorithm that would compute optimal grasps and might be integrated into a complete, state-of-the-art tele-operated system.

Skill-Oriented and Performance-Driven Adaptive Curricula for Training in Robot-Assisted Surgery Using Simulators: A Feasibility Study

OBJECTIVE

Virtual Reality (VR) simulators represent a remarkable educational opportunity in order to acquire and refine surgical practical skills. Nevertheless, there exists no consensus regarding a standard curriculum of simulation-based training. This study introduces an automatic, adaptive curriculum where the training session is real-time scheduled on the basis of the trainee's performances.

METHODS

An experimental study using the master console of the da Vinci Research Kit (Intuitive Surgical Inc., Sunnyvale, US) was carried out to test this approach. Tasks involving fundamental skills of robotic surgery were designed and simulated in VR. Twelve participants without medical background along with twelve medical residents were randomly and equally divided into two groups: a control group, self-managing the training session, and an experimental group, undergoing the proposed adaptive training.

RESULTS

The performances of the experimental users were significantly better with respect to the ones of the control group after training (non-medical: p < 0.01; medical: p = 0.02). This trend was analogous in the non-medical and medical populations and no significant difference was identified between these two classes (even in the baseline assessment).

CONCLUSION

The analysis of the learning of the involved surgical skills highlighted how the proposed adaptive training managed to better identify and compensate for the trainee's gaps. The absence of initial difference between the non-medical and medical users underlines that robotic surgical devices require specific training before clinical practice.

SIGNIFICANCE

This feasibility study could pave the way towards the improvement of simulation-based training curricula.

Dance training improves the CNS's ability to utilize the redundant degrees of freedom of the whole body

Professional dancers demonstrate an amazing ability to control their balance. However, little is known about how they coordinate their body segments for such superior control. In this study, we investigated how dancers coordinate body segments when a physical perturbation is given to their body. A custom-made machine was used to provide a short pulling impulse at the waist in the anterior direction to ten dancers and ten non-dancers. We used Uncontrolled Manifold analysis to quantify the variability in the task-relevant space and task-irrelevant space within the multi-dimensional space made up of individual segments’ centers of mass with a velocity adjustment. The dancers demonstrated greater utilization of redundant degrees of freedom (DoFs) supported by the greater task-irrelevant variability as compared to non-dancers. These findings suggest that long-term specialized dance training can improve the central nervous system’s ability to utilize the redundant DoFs in the whole-body system.

Using a Redundant User Interface in Teleoperated Surgical Systems for Task Performance Enhancement

The enhanced dexterity and manipulability offered by master–slave teleoperated surgical systems have significantly improved the performance and safety of minimally invasive surgeries. However, effective manipulation of surgical robots is sometimes limited due to the mismatch between the slave and master robots’ kinematics and workspace. The purpose of this paper is first to formulate a quantifiable measure of the combined master–slave system manipulability. Next, we develop a null-space controller for the redundant master robot that employs the proposed manipulability index to enhance the performance of teleoperation tasks by matching the kinematics of the redundant master robot with the kinematics of the slave robot. The null-space controller modulates the redundant degrees of freedom of the master robot to reshape its manipulability ellipsoid (ME) towards the ME of the slave robot. The ME is the geometric interpretation of the kinematics of a robot. By reshaping the master robot’s manipulability, we match the master and slave robots’ kinematics. We demonstrate that by using a redundant master robot, we are able to enhance the master–slave system manipulability and more intuitively transfer the slave robot’s dexterity to the user. Simulation and experimental studies are performed to validate the performance of the proposed control strategy. Results demonstrate that by employing the proposed manipulability index, we can enhance the user’s control over the force/velocity of a surgical robot and minimize the user’s control effort for a teleoperated task.

Dynamic Reconfiguration of Redundant Haptic Interfaces for Rendering Soft and Hard Contacts

There are conflicting objectives between required characteristics of haptic interfaces such as maximum force feedback capability versus back-drive friction, which can be optimally traded-off in a redundant haptic interface; a redundant haptic interface has more degrees of freedom than minimally required ones for a given task. In this article, a contact-aware null-space control approach for redundant haptic interfaces is proposed to address these trade-offs. First, we introduce a task-dependent null-space controller in which the internal motion of the redundant haptic interface is appropriately controlled to achieve a desired performance; i.e., low back-drive friction in case of free-space motion and soft contact or large force feedback capability in case of stiff contact. Next, a transition method is developed to facilitate the adaptation of the null-space controller's varying objectives according to the varying nature of the task. The transition method prevents discontinuities in the null-space control signal. This transition method is informed by a proposed actuator saturation observer that monitors the distance of joint torques from their saturation levels. The overall outcome is an ability to recreate the feelings of soft contacts and hard contacts with higher fidelity compared to what a conventional non-redundant haptic interface can achieve. Simulations are provided throughout the paper to illustrate the concepts. Moreover, experimental results are reported to verify the effectiveness of the proposed control strategies. It is shown that the proposed controller can perform well in the soft-contact, hard-contact, and transition phases.

The Trade-Off of Virtual Reality Training for Dart Throwing: A Facilitation of Perceptual-Motor Learning With a Detriment to Performance

Advancements in virtual reality (VR) technology now allow for the creation of highly immersive virtual environments and for systems to be commercially available at an affordable price. Despite increased availability, this access does not ensure that VR is appropriate for training for all motor skills. Before the implementation of VR for training sport-related skills takes place, it must first be established whether VR utilization is appropriate. To this end, it is crucial to better understand the mechanisms that drive learning in these new environments which will allow for optimization of VR to best facilitate transfer of learned skills to the real world. In this study we sought to examine how a skill acquired in VR compares to one acquired in the real world (RW), utilizing training to complete a dart-throwing task in either a virtual or real environment. We adopted a perceptual-motor approach in this study, employing measures of task performance (i.e., accuracy), as well as of perception (i.e., visual symptoms and oculomotor behavior) and motor behaviors (i.e., throwing kinematics and coordination). Critically, the VR-trained group performed significantly worse in terms of throwing accuracy compared to both the RW-trained group and their own baseline performance. In terms of perception, the VR-trained group reported greater acute visual symptoms compared to the RW-trained group, though oculomotor behaviors were largely the same across groups. In terms of motor behaviors, the VR-trained group exhibited different dart-throwing kinematics during training, but in the follow-up test adapted their throwing pattern to one similar to the RW-trained group. In total, VR training impaired real-world task performance, suggesting that virtual environments may offer different learning constraints compared to the real world. These results thus emphasize the need to better understand how some elements of virtual learning environments detract from transfer of an acquired sport skill to the real world. Additional work is warranted to further understand how perceptual-motor behaviors are acquired differently in virtual spaces.

Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions

Robotic assisted minimally invasive surgery (RAMIS) systems present many advantages to the surgeon and patient over open and standard laparoscopic surgery. However, haptic feedback, which is crucial for the success of many surgical procedures, is still an open challenge in RAMIS. Understanding the way that haptic feedback affects performance and learning can be useful in the development of haptic feedback algorithms and teleoperation control systems. In this study, we examined the performance and learning of inexperienced participants under different haptic feedback conditions in a task of surgical needle driving via a soft homogeneous deformable object-an artificial tissue. We designed an experimental setup to characterize their movement trajectories and the forces that they applied on the artificial tissue. Participants first performed the task in an open condition, with a standard surgical needle holder, followed by teleoperation in one of three feedback conditions: (1) no haptic feedback, (2) haptic feedback based on position exchange, and (3) haptic feedback based on direct recording from a force sensor, and then again with the open needle holder. To quantify the effect of different force feedback conditions on the quality of needle driving, we developed novel metrics that assess the kinematics of needle driving and the tissue interaction forces, and we combined our novel metrics with classical metrics. We analyzed the final teleoperated performance in each condition, the improvement during teleoperation, and the aftereffect of teleoperation on the performance when using the open needle driver. We found that there is no significant difference in the final performance and in the aftereffect between the 3 conditions. Only the two conditions with force feedback presented statistically significant improvement during teleoperation in several of the metrics, but when we compared directly between the improvements in the three different feedback conditions none of the effects reached statistical significance. We discuss possible explanations for the relative similarity in performance. We conclude that we developed several new metrics for the quality of surgical needle driving, but even with these detailed metrics, the advantage of state of the art force feedback methods to tasks that require interaction with homogeneous soft tissue is questionable.

Variability of running coordination in experts and novices: A 3D uncontrolled manifold analysis

The uncontrolled manifold (UCM) approach has been widely used in recent studies to examine variability in daily tasks; however, it has not yet been used to study running or the effects of expertise. Therefore, the aim of this study was to analyse the synergy structure stabilizing the centre of mass (CoM) trajectory in experts compared to novices during running at two different speeds using a subject-specific 3D model. A total of 25 healthy young adults (13 experts, 12 novices) participated in the study. All subjects ran at 10 and 15 km h^-1 on a treadmill. In each case, kinematics of 20 consecutive gait cycles were recorded and the effects of expertise and gait cycle phase on the synergy structure were investigated at both speeds. Specifically, the variance affecting the CoM ( U C M ⊥ ) , the variance not affecting the CoM ( U C M ∥ ) , and their ratio ( U C M R a t i o ) were analysed. Descriptively, in both groups there was a synergy stabilizing the CoM trajectory in running. However, the ANOVA showed no differences in U C M R a t i o between the two groups. In novices, U C M ⊥ and U C M ∥ were significantly higher compared to experts at the 15 km h^-1 condition. In both groups, there was more variability in the stance phase compared to the flight phase in the majority of cases. The results indicate that experts adopted a more consistent running style. The stride-to-stride variability was diminished but not abolished. This difference was only visible at the 15 km h^-1 condition. Furthermore, variability was less constrained in the stance phase compared to the flight phase.

FES Cycling in Stroke: Novel Closed-Loop Algorithm Accommodates Differences in Functional Impairments

OBJECTIVE

The objective of this paper was to develop and test a novel control algorithm that enables stroke survivors to pedal a cycle in a desired cadence range despite varying levels of functional abilities after stroke.

METHODS

A novel algorithm was developed which automatically adjusts 1) the intensity of functional electrical stimulation (FES) delivered to the leg muscles, and 2) the current delivered to an electric motor. The algorithm automatically switches between assistive, uncontrolled, and resistive modes to accommodate for differences in functional impairment, based on the mismatch between the desired and actual cadence. Lyapunov-based methods were used to theoretically prove that the rider's cadence converges to the desired cadence range. To demonstrate the controller's real-world performance, nine chronic stroke survivors performed two cycling trials: 1) volitional effort only and 2) volitional effort accompanied by the control algorithm assisting and resisting pedaling as needed.

RESULTS

With a desired cadence range of 50-55 r/min, the developed controller resulted in an average rms cadence error of 1.90 r/min, compared to 6.16 r/min during volitional-only trials.

CONCLUSION

Using FES and an electric motor with a two-sided cadence control objective to assist and resist volitional efforts enabled stroke patients with varying strength and abilities to pedal within a desired cadence range.

SIGNIFICANCE

A protocol design that constrains volitional movements with assistance and resistance from FES and a motor shows potential for FES cycles and other rehabilitation robots during stroke rehabilitation.

Automatic and near real-time stylistic behavior assessment in robotic surgery

PURPOSE

Automatic skill evaluation is of great importance in surgical robotic training. Extensive research has been done to evaluate surgical skill, and a variety of quantitative metrics have been proposed. However, these methods primarily use expert selected features which may not capture latent information in movement data. In addition, these features are calculated over the entire task time and are provided to the user after the completion of the task. Thus, these quantitative metrics do not provide users with information on how to modify their movements to improve performance in real time. This study focuses on automatic stylistic behavior recognition that has the potential to be implemented in near real time.

METHODS

We propose a sparse coding framework for automatic stylistic behavior recognition in short time intervals using only position data from the hands, wrist, elbow, and shoulder. A codebook is built for each stylistic adjective using the positive and negative labels provided for each trial through crowd sourcing. Sparse code coefficients are obtained for short time intervals (0.25 s) in a trial using this codebook. A support vector machine classifier is trained and validated through tenfold cross-validation using the sparse codes from the training set.

RESULTS

The results indicate that the proposed dictionary learning method is able to assess stylistic behavior performance in near real time using user joint position data with improved accuracy compared to using PCA features or raw data.

CONCLUSION

The possibility to automatically evaluate a trainee's style of movement in short time intervals could provide the user with online customized feedback and thus improve performance during surgical tasks.

Design and Evaluation of a Performance-based Adaptive Curriculum for Robotic Surgical Training: a Pilot Study

Training with simulation systems has become a primary alternative for learning the fundamental skills of robotic surgery. However, there exists no consensus regarding a standard training curriculum: sessions defined a priori by expert trainers or self-directed by the trainees feature lack of consistency. This study proposes an adaptive approach that structures the curriculum on the basis of an objective assessment of the trainee's performance. The work comprised an experimental session with 12 participants performing training on virtual reality tasks with the da Vinci Research Kit surgical console. Half of the subjects self-managed their training session, while the others underwent the adaptive training. The final performance of the latter trainees was found to be higher compared to the former (p=0.002), showing how outcome-based, dynamic designs could constitute a promising advance in robotic surgical training.

Dynamic changes of brain functional states during surgical skill acquisition

There is lack of a standardized measure of technical proficiency and skill acquisition for robot-assisted surgery (RAS). Learning surgical skills, in addition to the interaction with the machine and the new surgical environment adds to the complexity of the learning process. Moreover, evaluation of surgeon performance in operating room is required to optimize patient safety. In this study, we investigated the dynamic changes of RAS trainee’s brain functional states by practice. We also developed brain functional state measurements to find the relationship between RAS skill acquisition (especially human-machine interaction skills) and reconfiguration of brain functional states. This relationship may help in providing trainees with helpful, structured feedback regarding skills requiring improvement and will help in tailoring training activities.

On the coordination of highly dynamic human movements: an extension of the Uncontrolled Manifold approach applied to precision jump in parkour

The human body generally has more degrees of freedom than necessary for generating a given movement. According to the motor abundance principle, this redundancy is beneficial as it provides the central nervous system with flexibility and robustness for the generation of movements. Under the hypothesis of the Uncontrolled Manifold, the additional degrees of freedom are used to produce motor solutions by reducing the variability that affects the motion performance across repetitions. In this paper, we present a general mathematical framework derived from robotics to formulate kinematic and dynamic tasks in human movement. On this basis, an extension of the Uncontrolled Manifold approach is introduced to deal with dynamic movements. This extension allows us to present a complex experimental application of the proposed framework to highly dynamic task variables in parkour movements. This experiment involves dynamic tasks expressed in terms of linear and angular momenta. The results show that the central nervous system is able to coordinate such skilled tasks which appear to be preferentially controlled and hierarchically organized. The proposed extension is promising for the study of motion generation in anthropomorphic systems and provides a formal description to investigate kinematics and dynamics tasks in human motions.

An Uncontrolled Manifold Analysis of Arm Joint Variability in Virtual Planar Position and Orientation Telemanipulation

OBJECTIVE

In teleoperated robot-assisted tasks, the user interacts with manipulators to finely control remote tools. Manipulation of robotic devices, characterized by specific kinematic and dynamic proprieties, is a complex task for the human sensorimotor system due to the inherent biomechanical and neuronal redundancies that characterize the human arm and its control. We investigate how master devices with different kinematics structures and how different task constraints influence users capabilities in exploiting arm redundancy.

METHODS

A virtual teleoperation workbench was designed and the arm kinematics of seven users was acquired during the execution of two planar virtual tasks, involving either the control of position only or position-orientation of a tool. Using the uncontrolled manifold analysis of arm joint variability, we estimated the logarithmic ratio between the task irrelevant and the task relevant manifolds ( R_v).

RESULTS

The R_v values obtained in the position-orientation task were higher than in the position only task, while no differences were found between the master devices. A modulation of R_v was found through the execution of the position task and a positive correlation was found between task performance and redundancy exploitation.

CONCLUSION

Users exploited additional portions of arm redundancy when dealing with the tool orientation. The R_v modulation seems influenced by the task constraints and by the users possibility of reconfiguring the arm position.

SIGNIFICANCE

This paper advances the general understanding of the exploitation of arm redundancy in complex tasks, and can improve the development of future robotic devices.

Human-centric predictive model of task difficulty for human-in-the-loop control tasks

Quantitatively measuring the difficulty of a manipulation task in human-in-the-loop control systems is ill-defined. Currently, systems are typically evaluated through task-specific performance measures and post-experiment user surveys; however, these methods do not capture the real-time experience of human users. In this study, we propose to analyze and predict the difficulty of a bivariate pointing task, with a haptic device interface, using human-centric measurement data in terms of cognition, physical effort, and motion kinematics. Noninvasive sensors were used to record the multimodal response of human user for 14 subjects performing the task. A data-driven approach for predicting task difficulty was implemented based on several task-independent metrics. We compare four possible models for predicting task difficulty to evaluated the roles of the various types of metrics, including: (I) a movement time model, (II) a fusion model using both physiological and kinematic metrics, (III) a model only with kinematic metrics, and (IV) a model only with physiological metrics. The results show significant correlation between task difficulty and the user sensorimotor response. The fusion model, integrating user physiology and motion kinematics, provided the best estimate of task difficulty (R2 = 0.927), followed by a model using only kinematic metrics (R2 = 0.921). Both models were better predictors of task difficulty than the movement time model (R2 = 0.847), derived from Fitt's law, a well studied difficulty model for human psychomotor control.

Meaningful Assessment of Robotic Surgical Style using the Wisdom of Crowds

OBJECTIVE

Quantitative assessment of surgical skills is an important aspect of surgical training; however, the proposed metrics are sometimes difficult to interpret and may not capture the stylistic characteristics that define expertise. This study proposes a methodology for evaluating the surgical skill, based on metrics associated with stylistic adjectives, and evaluates the ability of this method to differentiate expertise levels.

METHODS

We recruited subjects from different expertise levels to perform training tasks on a surgical simulator. A lexicon of contrasting adjective pairs, based on important skills for robotic surgery, inspired by the global evaluative assessment of robotic skills tool, was developed. To validate the use of stylistic adjectives for surgical skill assessment, posture videos of the subjects performing the task, as well as videos of the task were rated by crowd-workers. Metrics associated with each adjective were found using kinematic and physiological measurements through correlation with the crowd-sourced adjective assignment ratings. To evaluate the chosen metrics' ability in distinguishing expertise levels, two classifiers were trained and tested using these metrics.

RESULTS

Crowd-assignment ratings for all adjectives were significantly correlated with expertise levels. The results indicate that naive Bayes classifier performs the best, with an accuracy of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] when classifying into four, three, and two levels of expertise, respectively.

CONCLUSION

The proposed method is effective at mapping understandable adjectives of expertise to the stylistic movements and physiological response of trainees.

Variability Analysis on Gestures for People With Quadriplegia

Gesture-based interfaces have become an effective control modality within the human computer interaction realm to assist individuals with mobility impairments in accessing technologies for daily living to entertainment. Recent studies have shown that gesture-based interfaces in tandem with gaming consoles are being used to complement physical therapies at rehabilitation hospitals and in their homes. Because the motor movements of individuals with physical impairments are different from persons without disabilities, the gesture sets required to operate those interfaces must be customized. This limits significantly the number and quality of available software environments for users with motor impairments. Previous work presented an analytic approach to convert an existing gesture-based interface designed for individuals without disabilities to be usable by people with motor disabilities. The objective of this paper is to include gesture variability analysis into the existing framework using robotics as an additional validation framework. Based on this, a physical metric (referred as work) was empirically obtained to compare the physical effort of each gesture. An integration method was presented to determine the accessible gesture set based on stability and empirical robot execution. For all the gesture types, the accessible gestures were found to lie within 34% of the optimality of stability and work. Lastly, the gesture set determined by the proposed methodology was practically evaluated by target users in experiments while solving a spatial navigational problem.

Uncontrolled manifold analysis of joint angle variability during table tennis forehand

This study was conducted with the objective of evaluating the variance structure of the trunk and racket arm joint angles in table tennis topspin forehand using the uncontrolled manifold (UCM) approach, regarding racket orientation as the task variable. Nine advanced and eight intermediate male collegiate table tennis players performed the topspin strokes against backspin balls. The trunk, upper limb, and racket were modeled as six rigid-link segments with a total of 16 rotation degrees of freedom. The UCM analysis was conducted using 30 trial datasets per participant to quantify the degree of redundancy exploitation needed to stabilize the vertical and horizontal angles of the racket. Irrespective of the performance level, the variance of the joint angle vector increased towards ball impact. The degree of redundancy exploitation increased towards ball impact. As a result, the variability of the racket angles was minimal at impact. Both groups of players used the relative movement between the racket and the hand to stabilize the racket angles at ball impact. The variance of the joint angle vector that affected the vertical racket face angle at ball impact was significantly smaller for advanced players than for intermediate players, and the degree of redundancy exploitation to stabilize that angle at impact tended to be larger for the advanced players. The ability to use the redundancy of the joint configuration to stabilize the vertical racket face angle at impact may be a critical factor that affects performance level.

Using Contact Forces and Robot Arm Accelerations to Automatically Rate Surgeon Skill at Peg Transfer

OBJECTIVE

Most trainees begin learning robotic minimally invasive surgery by performing inanimate practice tasks with clinical robots such as the Intuitive Surgical da Vinci. Expert surgeons are commonly asked to evaluate these performances using standardized five-point rating scales, but doing such ratings is time consuming, tedious, and somewhat subjective. This paper presents an automatic skill evaluation system that analyzes only the contact force with the task materials, the broad-bandwidth accelerations of the robotic instruments and camera, and the task completion time.

METHODS

We recruited N = 38 participants of varying skill in robotic surgery to perform three trials of peg transfer with a da Vinci Standard robot instrumented with our Smart Task Board. After calibration, three individuals rated these trials on five domains of the Global Evaluative Assessment of Robotic Skill (GEARS) structured assessment tool, providing ground-truth labels for regression and classification machine learning algorithms that predict GEARS scores based on the recorded force, acceleration, and time signals.

RESULTS

Both machine learning approaches produced scores on the reserved testing sets that were in good to excellent agreement with the human raters, even when the force information was not considered. Furthermore, regression predicted GEARS scores more accurately and efficiently than classification.

CONCLUSION

A surgeon's skill at robotic peg transfer can be reliably rated via regression using features gathered from force, acceleration, and time sensors external to the robot.

SIGNIFICANCE

We expect improved trainee learning as a result of providing these automatic skill ratings during inanimate task practice on a surgical robot.

Differential analysis of muscle fatigue induced elbow and wrist tremor in controlled laparoscopic manoeuvring

BACKGROUND

Fatigue induced hand tremor (FIT) is a primary limiting concern for the prolonged surgical intervention in minimally invasive surgery (MIS) and robot-assisted-minimally invasive surgery (RAMIS). A thorough analysis is necessary to understand the FIT characteristics in laparoscopic tool movement. The primary aim of this study is to perform a differential analysis of the elbow and wrist tremor due to muscle fatigue in laparoscopic manoeuvring.

METHODS

We have introduced a joint angle based tremor analysis method, which enables us to perform a differential study of FIT characteristics at the individual joint. Experimental data was acquired from a group of subjects during static and dynamic laparoscopic movement in an imitative RAMIS master manipulation scenario. A repetitive task was performed with a total span of 1 h for observing the effect of muscle fatigue. Along with the joint angle variation, surface electromyography (sEMG) signal was also studied in the analysis.

RESULTS

The wrist tremor is more predominant than tremor generated at the elbow, especially in highly fatigued condition. The high-frequency tremor (>4 Hz) is contributed by the wrist joint. Moreover, the variation of the wrist and elbow tremor ratio was found to be dependent upon the experience of the surgeons.

CONCLUSIONS

In this work, we have investigated the attribution of elbow and wrist joints in FIT during laparoscopic tool manipulation. The outcomes may be useful for the design of robot-assisted surgical manipulator, and can be used for quality assessment of surgical training as well.

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.