Motion analysis for better understanding of psychomotor skills in laparoscopy: objective assessment-based simulation training using animal organs

Validation and motion analyses of laparoscopic radical nephrectomy with Thiel-embalmed cadavers

PURPOSE

Our aim was to develop practical training for laparoscopic surgery using Thielembalmed cadavers. Furthermore, in order to verbalize experts' motion characteristics and provide objective feedback to trainees, we initiated motion capture analyses of multiple surgical instruments simultaneously during the cadaveric trainings. In the present study, we report our preliminary results.

METHODS

Participants voluntarily joined the present cadaveric simulation trainings, and performed laparoscopic radical nephrectomy. After the trainings, scores for tissue similarity (face validity) and impression of educational merit (content validity) were collected from participants based on a 5-point Likert scale (tissue similarity: 5: very similar, 3: average, 1: very different; educational merit: 5: very high, 3: average, 1: very low). In addition, after the additional IRB approval, we started motion capture (Mocap) analyses of 6 surgical instruments (scissors, vessel sealing system, grasping forceps, clip applier, right-angled forceps, and suction), using an infrared trinocular camera (120-Hz location record). Mocap-metrics were compared according to the previous surgical experiences (experts: ≧50 laparoscopic surgeries, intermediates: 10-49, novices: 0-9), using the Kruskal-Wallis test.

RESULTS

A total of 9 experts, 19 intermediates, and 15 novices participated in the present study. In terms of face validity, the mean scores were higher than 3, other than for the Vena cava(mean score of 2.89). Participants agreed with the training value (usefulness for future skill improvement: mean score of 4.57). In terms of Mocap analysis, faster speed-related metrics (e.g., velocity, the distribution of tip velocity, acceleration, and jerk) in the scissors and vessel sealing system, a shorter path length of grasping forceps, and fewer dimensionless squared jerks, which indicated more purposeful motion of 4 surgical instruments (vessel sealing system, grasping forceps, clip applier and suction), were observed in the more experienced group.

CONCLUSIONS

The Thiel-embalmed cadaver provides an excellent training opportunity for complex laparoscopic procedures with participants' high level of satisfaction, and may become a promising tool for a better objective understanding of surgical dexterity. In order to enrich formative feedback to trainees, we are now proceeding with Mocap analysis.

Estimating Cognitive Workload Using Task-Related Pupillary Responses in Simulated Drilling in Cochlear Implantation

OBJECTIVES

Training of temporal bone drilling requires more than mastering technical skills with the drill. Skills such as visual imagery, bimanual dexterity, and stress management need to be mastered along with precise knowledge of anatomy. In otorhinolaryngology, these psychomotor skills underlie performance in the drilling of the temporal bone for access to the inner ear in cochlear implant surgery. However, little is known about how psychomotor skills and workload management impact the practitioners' continuous and overall performance.

METHODS

To understand how the practitioner's workload and performance unfolds over time, we examine task-evoked pupillary responses (TEPR) of 22 medical students who performed transmastoid-posterior tympanotomy (TMPT) and removal of the bony overhang of the round window niche in a 3D-printed model of the temporal bone. We investigate how students' TEPR metrics (Average Pupil Size [APS], Index of Pupil Activity [IPA], and Low/High Index of Pupillary Activity [LHIPA]) and time spent in drilling phases correspond to the performance in key drilling phases.

RESULTS

All TEPR measures revealed significant differences between key drilling phases that corresponded to the anticipated workload. Enlarging the facial recess lasted significantly longer than other phases. IPA captured significant increase of workload in thinning of the posterior canal wall, while APS revealed increased workload during the drilling of the bony overhang.

CONCLUSION

Our findings contribute to the contemporary competency-based medical residency programs where objective and continuous monitoring of participants' progress allows to track progress in expertise acquisition. Laryngoscope, 2024.

Surgeon Upper Extremity Kinematics During Error and Error-Free Retropubic Trocar Passage

INTRODUCTION AND HYPOTHESIS

Surgeon kinematics play a significant role in the prevention of patient injury. We hypothesized that elbow extension and ulnar wrist deviation are associated with bladder injury during simulated midurethral sling (MUS) procedures.

METHODS

We used motion capture technology to measure surgeons' flexion/extension, abduction/adduction, and internal/external rotation angular time series for shoulder, elbow, and wrist joints. Starting and ending angles, minimum and maximum angles, and range of motion (ROM) were extracted from each time series. We created anatomical multibody models and applied linear mixed modeling to compare kinematics between trials with versus without bladder penetration and attending versus resident surgeons. A total of 32 trials would provide 90% power to detect a difference.

RESULTS

Out of 85 passes, 62 were posterior to the suprapubic bone and 20 penetrated the bladder. Trials with versus without bladder penetration were associated with more initial wrist dorsiflexion (-27.32 vs -9.03°, p = 0.01), less final elbow flexion (39.49 vs 60.81, p = 0.03), and greater ROM in both the wrist (27.48 vs 14.01, p = 0.02), and elbow (20.45 vs 12.87, p = 0.04). Wrist deviation and arm pronation were not associated with bladder penetration. Compared with attendings, residents had more ROM in elbow flexion (14.61 vs 8.35°, p < 0.01), but less ROM in wrist dorsiflexion (13.31 vs 20.33, p = 0.02) and arm pronation (4.75 vs 38.46, p < 0.01).

CONCLUSIONS

Bladder penetration during MUS is associated with wrist dorsiflexion and elbow flexion but not internal wrist deviation and arm supination. Attending surgeons exerted control with the wrist and forearm, surgical trainees with the elbow. Our findings have direct implications for MUS teaching.

Development of a synchronous motion-tracking and video capture tool for flexible ureteroscopy

INTRODUCTION

Hand/instrument motion-tracking in surgical simulation provides valuable data to improve psychomotor skills and can serve as a formative evaluation tool. Motion analysis has been well-studied in laparoscopic surgery; however, there are essentially no studies looking at motion-tracking for flexible ureteroscopy (fURS ), a common surgical procedure requiring hand dexterity and 3D spatial awareness. We aimed to design a synchronized motion-tracking and video capture system for fURS capable of collecting objective metrics for use in surgical skills training.

METHODS

Motion tracking of the ureteroscope was performed using a motion-tracking platform, inertial measurement units (IMUs), and an optical sensor. Position (x, y, z) and orientation (roll, pitch, yaw) of the ureteroscope handle, lever deflection, and translation of the scope insertion point were collected. Video capture of the operator's hands was collected with a Raspberry Pi camera. All peripherals were controlled on a Raspberry Pi 4 and synchronized to its system clock.

RESULTS

Our system demonstrated good accuracy in detecting translation of the ureteroscope in the x- and y-axes, and yaw, pitch and roll of the ureteroscope at discrete orientations of 0, ±30, ±60, and ±90 degrees. Unique to fURS, deflection of the lever was captured by the difference in IMU static accelerations with good accuracy. The optical sensor detected translation of the ureteroscope at the insertion point with good precision and an average error of 5.51%.

CONCLUSIONS

We successfully developed a motion-tracking and video-capture system capable of collecting motion-analysis parameters unique to fURS . Future studies will focus on establishing the construct validity of this tool.

An objective skill assessment framework for microsurgical anastomosis based on ALI scores

INTRODUCTION

The current assessment and standardization of microsurgical skills are subjective, posing challenges in reliable skill evaluation. We aim to address these limitations by developing a quantitative and objective framework for accurately assessing and enhancing microsurgical anastomosis skills among surgical trainees. We hypothesize that this framework can differentiate the proficiency levels of microsurgeons, aligning with subjective assessments based on the ALI score.

METHODS

We select relevant performance metrics from the literature on laparoscopic skill assessment and human motor control studies, focusing on time, instrument kinematics, and tactile information. This information is measured and estimated by a set of sensors, including cameras, a motion capture system, and tactile sensors. The recorded data is analyzed offline using our proposed evaluation framework. Our study involves 12 participants of different ages ([Formula: see text] years) and genders (nine males and three females), including six novice and six intermediate subjects, who perform surgical anastomosis procedures on a chicken leg model.

RESULTS

We show that the proposed set of objective and quantitative metrics to assess skill proficiency aligns with subjective evaluations, particularly the ALI score method, and can effectively differentiate novices from more proficient microsurgeons. Furthermore, we find statistically significant disparities, where microsurgeons with intermediate level of skill proficiency surpassed novices in both task speed, reduced idle time, and smoother, briefer hand displacements.

CONCLUSION

The framework enables accurate skill assessment and provides objective feedback for improving microsurgical anastomosis skills among surgical trainees. By overcoming the subjectivity and limitations of current assessment methods, our approach contributes to the advancement of surgical education and the development of aspiring microsurgeons. Furthermore, our framework emerges to precisely distinguish and classify proficiency levels (novice and intermediate) exhibited by microsurgeons.

An intelligent grasper to provide real-time force feedback to shorten the learning curve in laparoscopic training

BACKGROUND

A lack of force feedback in laparoscopic surgery often leads to a steep learning curve to the novices and traditional training system equipped with force feedback need a high educational cost. This study aimed to use a laparoscopic grasper providing force feedback in laparoscopic training which can assist in controlling of gripping forces and improve the learning processing of the novices.

METHODS

Firstly, we conducted a pre-experiment to verify the role of force feedback in gripping operations and establish the safe gripping force threshold for the tasks. Following this, we proceeded with a four-week training program. Unlike the novices without feedback (Group A2), the novices receiving feedback (Group B2) underwent training that included force feedback. Finally, we completed a follow-up period without providing force feedback to assess the training effect under different conditions. Real-time force parameters were recorded and compared.

RESULTS

In the pre-experiment, we set the gripping force threshold for the tasks based on the experienced surgeons' performance. This is reasonable as the experienced surgeons have obtained adequate skill of handling grasper. The thresholds for task 1, 2, and 3 were set as 0.731 N, 1.203 N and 0.938 N, respectively. With force feedback, the gripping force applied by the novices with feedback (Group B1) was lower than that of the novices without feedback (Group A1) (p < 0.005). During the training period, the Group B2 takes 6 trails to achieve gripping force of 0.635 N, which is lower than the threshold line, whereas the Group A2 needs 11 trails, meaning that the learning curve of Group B2 was significantly shorter than that of Group A2. Additionally, during the follow-up period, there was no significant decline in force learning, and Group B2 demonstrated better control of gripping operations. The training with force feedback received positive evaluations.

CONCLUSION

Our study shows that using a grasper providing force feedback in laparoscopic training can help to control the gripping force and shorten the learning curve. It is anticipated that the laparoscopic grasper equipped with FBG sensor is promising to provide force feedback during laparoscopic training, which ultimately shows great potential in laparoscopic surgery.

The influence of prolonged instrument manipulation on gas leakage through trocars

BACKGROUND

During laparoscopic surgery, CO2 insufflation gas could leak from the intra-abdominal cavity into the operating theater. Medical staff could therefore be exposed to hazardous substances present in leaked gas. Although previous studies have shown that leakage through trocars is a contributing factor, trocar performance over longer periods remains unclear. This study investigates the influence of prolonged instrument manipulation on gas leakage through trocars.

METHODS

Twenty-five trocars with diameters ranging from 10 to 15 mm were included in the study. An experimental model was developed to facilitate instrument manipulation in a trocar under loading. The trocar was mounted to a custom airtight container insufflated with CO2 to a pressure of 15 mmHg, similar to clinical practice. A linear stage was used for prolonged instrument manipulation. At the same time, a fixed load was applied radially to the trocar cannula to mimic the reaction force of the abdominal wall. Gas leakage was measured before, after, and during instrument manipulation.

RESULTS

After instrument manipulation, leakage rates per trocar varied between 0.0 and 5.58 L/min. No large differences were found between leakage rates before and after prolonged manipulation in static and dynamic measurements. However, the prolonged instrument manipulation did cause visible damage to two trocars and revealed unintended leakage pathways in others that can be related to production flaws.

CONCLUSION

Prolonged instrument manipulation did not increase gas leakage rates through trocars, despite damage to some individual trocars. Nevertheless, gas leakage through trocars occurs and is caused by different trocar-specific mechanisms and design issues.

An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery

Surgical skill assessment can quantify the quality of the surgical operation via the motion state of the surgical instrument tip (SIT), which is considered one of the effective primary means by which to improve the accuracy of surgical operation. Traditional methods have displayed promising results in skill assessment. However, this success is predicated on the SIT sensors, making these approaches impractical when employing the minimally invasive surgical robot with such a tiny end size. To address the assessment issue regarding the operation quality of robot-assisted minimally invasive surgery (RAMIS), this paper proposes a new automatic framework for assessing surgical skills based on visual motion tracking and deep learning. The new method innovatively combines vision and kinematics. The kernel correlation filter (KCF) is introduced in order to obtain the key motion signals of the SIT and classify them by using the residual neural network (ResNet), realizing automated skill assessment in RAMIS. To verify its effectiveness and accuracy, the proposed method is applied to the public minimally invasive surgical robot dataset, the JIGSAWS. The results show that the method based on visual motion tracking technology and a deep neural network model can effectively and accurately assess the skill of robot-assisted surgery in near real-time. In a fairly short computational processing time of 3 to 5 s, the average accuracy of the assessment method is 92.04% and 84.80% in distinguishing two and three skill levels. This study makes an important contribution to the safe and high-quality development of RAMIS.

Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions

The analysis of functional upper extremity (UE) movement kinematics has implications across domains such as rehabilitation and evaluating job-related skills. Using movement kinematics to quantify movement quality and skill is a promising area of research but is currently not being used widely due to issues associated with cost and the need for further methodological validation. Recent developments by computationally-oriented research communities have resulted in potentially useful methods for evaluating UE function that may make kinematic analyses easier to perform, generally more accessible, and provide more objective information about movement quality, the importance of which has been highlighted during the COVID-19 pandemic. This narrative review provides an interdisciplinary perspective on the current state of computer-assisted methods for analyzing UE kinematics with a specific focus on how to make kinematic analyses more accessible to domain experts. We find that a variety of methods exist to more easily measure and segment functional UE movement, with a subset of those methods being validated for specific applications. Future directions include developing more robust methods for measurement and segmentation, validating these methods in conjunction with proposed kinematic outcome measures, and studying how to integrate kinematic analyses into domain expert workflows in a way that improves outcomes.

Tracking and evaluating motion skills in laparoscopy with inertial sensors

BACKGROUND

Analysis of surgical instrument motion is applicable in surgical skill assessment and monitoring of the learning progress in laparoscopy. Current commercial instrument tracking technology (optical or electromagnetic) has specific limitations and is expensive. Therefore, in this study, we apply inexpensive, off-the-shelf inertial sensors to track laparoscopic instruments in a training scenario.

METHODS

We calibrated two laparoscopic instruments to the inertial sensor and investigated its accuracy on a 3D-printed phantom. In a user study during a one-week laparoscopy training course with medical students and physicians, we then documented and compared the training effect in laparoscopic tasks on a commercially available laparoscopy trainer (Laparo Analytic, Laparo Medical Simulators, Wilcza, Poland) and the newly developed tracking setup.

RESULTS

Eighteen participants (twelve medical students and six physicians) participated in the study. The student subgroup showed significantly poorer results for the count of swings (CS) and count of rotations (CR) at the beginning of the training compared to the physician subgroup (p = 0.012 and p = 0.042). After training, the student subgroup showed significant improvements in the rotatory angle sum, CS, and CR (p = 0.025, p = 0.004 and p = 0.024). After training, there were no significant differences between medical students and physicians. There was a strong correlation between the measured learning success (LS) from the data of our inertial measurement unit system (LS_IMU) and the Laparo Analytic (LS_Lap) (Pearson's r = 0.79).

CONCLUSION

In the current study, we observed a good and valid performance of inertial measurement units as a possible tool for instrument tracking and surgical skill assessment. Moreover, we conclude that the sensor can meaningfully examine the learning progress of medical students in an ex-vivo setting.

An Ex Situ Cadaver Liver Training Model Continuously Pressurized to Simulate Specific Skills Involved in Laparoscopic Liver Resection: the Lap-Liver Trainer

BACKGROUND

Laparoscopic liver resection (LLR) requires delicate skills. The aim of the study was to develop a training model mimicking as much as possible intraoperative bleeding and bile leakage during LLR. We also assessed the educational value of the training model.

METHODS

The Lap-liver trainer (LLT) combined a continuously pressurized ex situ cadaver liver and a customized mannequin. The customized mannequin was designed by computer-aided design and manufactured by 3D printing. The left lateral sectionectomy (LLS) was chosen to assess the feasibility of a LLR with the LLT. Eighteen volunteers were recruited to perform LLS and to assess the educational value of the LLT using a Likert scale.

RESULTS

The customized mannequin consisted of a close laparoscopic training device based on a simplified reconstruction of the abdominal cavity in laparoscopic conditions. Ex situ cadaver livers were pressurized to simulate blood and bile supplies. Each expert surgeon (n = 3) performed two LLS. They were highly satisfied of simulation conditions (4.80 ± 0.45) and strongly recommended that the LLT should be incorporated into a teaching program (5.00 ± 0.0). Eight novice and 4 intermediate surgeons completed a teaching program and performed a LLS. Overall, the level of satisfaction was high (4.92 ± 0.29), and performing such a procedure under simulation conditions benefited their learning and clinical practice (4.92 ± 0.29).

CONCLUSIONS

The LLT could provide better opportunities for trainees to acquire and practice LLR skills in a more realistic environment and to improve their ability to deal with specific events related to LLR.

Utilizing the metaverse in anatomy and physiology

Of the many disruptive technologies being introduced within modern curricula, the metaverse, is of particular interest for its ability to transform the environment in which students learn. The modern metaverse refers to a computer-generated world which is networked, immersive, and allows users to interact with others by engaging a number of senses (including eyesight, hearing, kinesthesia, and proprioception). This multisensory involvement allows the learner to feel part of the virtual environment, in a way that somewhat resembles real-world experiences. Socially, it allows learners to interact with others in real-time regardless of where on earth they are located. This article outlines 20 use-cases where the metaverse could be employed within a health sciences, medicine, anatomy, and physiology disciplines, considering the benefits for learning and engagement, as well as the potental risks.

Automatic assessment of laparoscopic surgical skill competence based on motion metrics

The purpose of this study was to characterize the motion features of surgical devices associated with laparoscopic surgical competency and build an automatic skill-credential system in porcine cadaver organ simulation training. Participants performed tissue dissection around the aorta, dividing vascular pedicles after applying Hem-o-lok (tissue dissection task) and parenchymal closure of the kidney (suturing task). Movements of surgical devices were tracked by a motion capture (Mocap) system, and Mocap-metrics were compared according to the level of surgical experience (experts: ≥50 laparoscopic surgeries, intermediates: 10-49, novices: 0-9), using the Kruskal-Wallis test and principal component analysis (PCA). Three machine-learning algorithms: support vector machine (SVM), PCA-SVM, and gradient boosting decision tree (GBDT), were utilized for discrimination of the surgical experience level. The accuracy of each model was evaluated by nested and repeated k-fold cross-validation. A total of 32 experts, 18 intermediates, and 20 novices participated in the present study. PCA revealed that efficiency-related metrics (e.g., path length) significantly contributed to PC 1 in both tasks. Regarding PC 2, speed-related metrics (e.g., velocity, acceleration, jerk) of right-hand devices largely contributed to the tissue dissection task, while those of left-hand devices did in the suturing task. Regarding the three-group discrimination, in the tissue dissection task, the GBDT method was superior to the other methods (median accuracy: 68.6%). In the suturing task, SVM and PCA-SVM methods were superior to the GBDT method (57.4 and 58.4%, respectively). Regarding the two-group discrimination (experts vs. intermediates/novices), the GBDT method resulted in a median accuracy of 72.9% in the tissue dissection task, and, in the suturing task, the PCA-SVM method resulted in a median accuracy of 69.2%. Overall, the mocap-based credential system using machine-learning classifiers provides a correct judgment rate of around 70% (two-group discrimination). Together with motion analysis and wet-lab training, simulation training could be a practical method for objectively assessing the surgical competence of trainees.

Automated Microsurgical Tool Segmentation and Characterization in Intra-Operative Neurosurgical Videos

Checklist based routine evaluation of surgical skills in any medical school demands quality time and effort from the supervising expert and is highly influenced by assessor bias. Alternatively, automated video based surgical skill assessment is a simple and viable method to analyse surgical dexterity offline without the need for acute presence of an expert surgeon throughout the surgery. In this paper, a novel approach and results for the automated segmentation of microsurgical instruments from the real-world neurosurgical video dataset was presented. The proposed tool segmentation model showcased mean average precision of 96.7% in detecting, and localizing five surgical instruments from the real-world neurosurgical videos. Accurate detection and characterization of motion features of the microsurgical tool from the novel annotated neurosurgical video dataset forms the key step towards automated surgical skill evaluation. Clinical Relevance- Tool segmentation, localization, and characterization in neurosurgical video, has several applications including assessing surgeons skills, training novice surgeons, understanding critical operating procedures post surgery, characterizing any critical anatomical response to the tool that leads to the success or failure of the surgery, and building models for conducting autonomous robotic surgery. Semantic segmentation, and characterization of the microsurgical tools forms the basis of the modern neurosurgery.

Objective evaluation of laparoscopic surgical skills in wet lab training based on motion analysis and machine learning

BACKGROUND

Our aim was to build a skill assessment system, providing objective feedback to trainees based on the motion metrics of laparoscopic surgical instruments.

METHODS

Participants performed tissue dissection around the aorta (tissue dissection task) and renal parenchymal closure (parenchymal-suturing task), using swine organs in a box trainer under a motion capture (Mocap) system. Two experts assessed the recorded movies, according to the formula of global operative assessment of laparoscopic skills (GOALS: score range, 5-25), and the mean scores were utilized as objective variables in the regression analyses. The correlations between mean GOALS scores and Mocap metrics were evaluated, and potential Mocap metrics with a Spearman's rank correlation coefficient value exceeding 0.4 were selected for each GOALS item estimation. Four regression algorithms, support vector regression (SVR), principal component analysis (PCA)-SVR, ridge regression, and partial least squares regression, were utilized for automatic GOALS estimation. Model validation was conducted by nested and repeated k-fold cross validation, and the mean absolute error (MAE) was calculated to evaluate the accuracy of each regression model.

RESULTS

Forty-five urologic, 9 gastroenterological, and 3 gynecologic surgeons, 4 junior residents, and 9 medical students participated in the training. In both tasks, a positive correlation was observed between the speed-related parameters (e.g., velocity, velocity range, acceleration, jerk) and mean GOALS scores, with a negative correlation between the efficiency-related parameters (e.g., task time, path length, number of opening/closing operations) and mean GOALS scores. Among the 4 algorithms, SVR showed the highest accuracy in the tissue dissection task ([Formula: see text]), and PCA-SVR in the parenchymal-suturing task ([Formula: see text]), based on 100 iterations of the validation process of automatic GOALS estimation.

CONCLUSION

We developed a machine learning-based GOALS scoring system in wet lab training, with an error of approximately 1-2 points for the total score, and motion metrics that were explainable to trainees. Our future challenges are the further improvement of onsite GOALS feedback, exploring the educational benefit of our model and building an efficient training program.

Motion-Tracking Machines and Sensors: Advancing Education Technology

Graduate medical education is predominantly based on a time-based apprenticeship model, with implied acquisition of proficiency after a pre-set amount of clinical exposure. While motion metrics have been used previously to measure skill performance indicators, these assessments have largely been performed on a summative scale to describe the performance of complete tasks or procedures. By segmenting performances of interest and assessing the essential elements individually, a more comprehensive understanding of the aspects in need of improvement for a learner can be obtained. The purpose of this review is to discuss technologies applicable to motion tracking, their benefits and limitations, approaches to data processing, and potential applications based on recent improvements in this technology. Objective analysis of motion metrics may improve educational standards of learning and efficiency by both standardizing the feedback process for trainees and reducing the volume of instructors required to facilitate practice sessions. With rigorous validation and standardization, motion metric assessment may also prove useful to demonstrate competency in technical procedures as part of a comprehensive certification process.

Biomechanical analysis of expert anesthesiologists and novice residents performing a simulated central venous access procedure

BACKGROUND

Central venous access (CVA) is a frequent procedure taught in medical residencies. However, since CVA is a high-risk procedure requiring a detailed teaching and learning process to ensure trainee proficiency, it is necessary to determine objective differences between the expert's and the novice's performance to guide novice practitioners during their training process. This study compares experts' and novices' biomechanical variables during a simulated CVA performance.

METHODS

Seven experts and seven novices were part of this study. The participants' motion data during a CVA simulation procedure was collected using the Vicon Motion System. The procedure was divided into four stages for analysis, and each hand's speed, acceleration, and jerk were obtained. Also, the procedural time was analyzed. Descriptive analysis and multilevel linear models with random intercept and interaction were used to analyze group, hand, and stage differences.

RESULTS

There were statistically significant differences between experts and novices regarding time, speed, acceleration, and jerk during a simulated CVA performance. These differences vary significantly by the procedure stage for right-hand acceleration and left-hand jerk.

CONCLUSIONS

Experts take less time to perform the CVA procedure, which is reflected in higher speed, acceleration, and jerk values. This difference varies according to the procedure's stage, depending on the hand and variable studied, demonstrating that these variables could play an essential role in differentiating between experts and novices, and could be used when designing training strategies.