Kinematics in Surgical Training and Proficiency Assessment

The art of surgery has always been defined by the surgeon's hands—their steadiness, precision, and efficiency of movement. The assessment of surgical skill has remained largely subjective for centuries, relying on the experienced eye of mentors and peers. Today, the emerging field of objective performance indicators including surgical kinematics is transforming this paradigm, offering metrics to quantify the very essence of surgical skill and mastery.¹ 

Understanding Surgical Kinematics 

Surgical kinematics refers to the precise measurement and analysis of hand and instrument movements during surgical procedures. It encompasses several key dimensions: 

• Smoothness and precision of motion: The evaluation of unnecessary movements and tremors that distinguish novice from expert surgeons 

• Speed and acceleration profiles: The rhythmic cadence of surgical actions, revealing efficiency and decisiveness 

• Depth perception and spatial awareness: The three-dimensional understanding of anatomical relationships that guides safe and effective intervention 

The study of surgical movement dates back to the late 1990s when researchers began using electromagnetic tracking systems to analyze hand motions during simulated tasks.² These early studies revealed that expert surgeons consistently demonstrated more efficient movement patterns than novices. Our ability to capture increasingly detailed kinematic data evolved with technology—from basic path length and time measurements to complex vector analyses of acceleration, jerk, and directional control. 

Surgical Safety Technologies' OR Black Box® now features kinematic data integration for laparoscopic surgery video assessment within its Explorer™ module, which represents an important step in this direction. Researchers are working to tie laparoscopic surgery kinematic data to real clinical outcomes, building upon previous work in open surgery hand kinematics and robotic applications to provide unprecedented insights into technical performance using surgical video recordings.

This transition from subjective assessment ("This surgeon has good hands") to objective measurement ("This surgeon's movements show 30% less path deviation than the average") represents a fundamental shift in how we conceptualize, teach, and evaluate surgical skill.

The Holy Grail: Technical Competency Assessment 

Current methods of assessing surgical competency face significant limitations. Global rating scales, procedure logs, and subjective faculty evaluations all suffer from the same fundamental challenge: they rely heavily on human subjective judgment, which introduces variability and potential bias into the assessment process. 

Kinematics is poised to revolutionize surgical training³ by providing quantifiable metrics that correlate with surgical expertise and meaningful clinical outcomes. Studies have consistently shown that expert surgeons demonstrate:⁴

• Shorter path lengths (more direct movements) 

• Smoother velocity profiles (fewer starts and stops) 

• More consistent depth perception control 

• Greater economy of motion (fewer unnecessary movements) 

Dr. Carla Pugh, a pioneer in surgical assessment technology, is a strong proponent of using advanced engineering technology to quantify surgical mastery and shorten the learning curve. Her groundbreaking research explores the visual-haptic loop that expert surgeons develop,⁵ referring to the continuous feedback cycle between what surgeons see (visual input) and what they feel through their hands (tactile or haptic feedback), allowing them to make real-time adjustments based on tissue resistance, anatomical landmarks, and other sensory cues that inform their technical approach. This integrated sensory-motor process becomes increasingly refined with experience, enabling master surgeons to anticipate tissue responses and make precise decisions that less experienced practitioners might miss. 

These metrics provide an objective foundation for assessing technical competency that complements traditional surgical training evaluation methods. Rather than replacing the experienced judgment of surgical educators, kinematic analysis enhances it with quantifiable data points that can be tracked over time.  

Potential Applications of Kinematics in Surgical Training 

The integration of kinematic analysis into surgical training⁶ opens exciting possibilities for enhancing education and assessment. 

Data-Driven Training Protocols 

Kinematic data allows educators to identify specific movement patterns that differentiate expert from novice performance. This insight can inform targeted surgical training protocols designed to develop these specific skills. 

Dr. Andrew Hung's pioneering work on the kinematics of robotic urologic procedures has demonstrated how movement analysis can inform specific training interventions and influence clinical outcomes. His research employs artificial intelligence to analyze robotic surgery data—including instrument movements, wrist articulation, and surgical gestures—revealing that technical performance metrics strongly predict patient outcomes. Furthermore, experienced surgeons demonstrate distinctive efficiency patterns during critical steps like during anastomoses and dissection that can be captured through automated performance metrics using computer vision to create targeted surgical training protocols.⁷ 

Perhaps the most transformative application of kinematics is the potential for real-time feedback during surgical training. Imagine a simulation system that highlights inefficient movements as they occur, providing immediate guidance to trainees. 

Such systems are already emerging in virtual reality and home-based robotic training platforms.⁸ They analyze movement patterns and provide feedback on metrics like path length, economy of motion, and instrument control—allowing trainees to make immediate corrections and gradually internalize more efficient movement patterns. 

Establishing Proficiency Benchmarks 

Although still an aspirational goal for surgical board certification organizations, use of kinematic analysis from expert surgeons enables the development of objective proficiency benchmarks. Instead of relying solely on procedure counts, future trainees could be required to demonstrate ideal movement patterns within defined proficiency ranges. Prospective validation of these benchmarks is still needed to ensure they support meaningful educational outcomes and are generalizable across diverse surgical practice patterns. 

This approach has been successfully implemented in some robotic training programs, where trainees must meet specific kinematic thresholds before progressing to more complex procedures or unsupervised practice.⁹ 

Simulation-Based Training 

Kinematic data has already revolutionized simulation-based surgical training by providing objective metrics for performance assessment. But the next frontier involves using this data to create more sophisticated simulation scenarios that adaptively respond to the trainee's movement patterns.¹⁰ 

Personalized Learning Pathways 

Perhaps most exciting is the potential for kinematic analysis to enable truly personalized surgical training pathways.¹¹ By identifying each trainee's specific movement patterns, educators could develop targeted interventions tailored to their unique strengths and weaknesses. 

This approach moves beyond the one-size-fits-all model of surgical training toward a more personalized paradigm—one that acknowledges the diversity of learning styles and physical capabilities among surgical trainees. 

Future Potential 

The future of surgical kinematics is inextricably linked to advances in machine learning (ML) and artificial intelligence. These technologies promise to transform how we collect, analyze, and interpret kinematic data. 

Machine Learning Applications 

ML algorithms can identify subtle patterns in kinematic data that might escape human observation. This could reveal new insights into the nature of surgical expertise and how it develops over time.¹² 

Preliminary research suggests that ML algorithms can predict surgical skill levels with remarkable accuracy based solely on movement data. The increasing sophistication of these algorithms may enable the identification of specific technical issues and recommended target interventions. 

Automated Performance Assessment 

The ultimate goal for many researchers is to develop fully automated performance assessment systems based on kinematic analysis.¹³ Such systems would provide objective, consistent evaluation of technical skill—complementing but not replacing human judgment. The rapid advancement of computer vision and motion analysis technologies suggests it may be achievable within the next decade. 

Impact on Training, Certification, and Quality Control 

Kinematic analysis will become more sophisticated and widely accepted over time, and it could fundamentally transform surgical training, certification, and quality control processes. 

Residency programs may adopt kinematic proficiency benchmarks as graduation requirements, while certification boards could integrate other similar objective performance indicators into board certification criteria. Hospitals might also implement kinematic monitoring for quality control, particularly in high-risk procedures. These proposals remain hotly debated within the surgical community, prompting important discussions about the utility of such benchmarks and their potential to ensure a safe, competent surgical workforce equipped to meet future healthcare demands. 

The Human Element 

Despite these technological advances, it's important to remember that surgery remains fundamentally human. The art of surgical practice extends beyond technical skill to include clinical judgment, communication abilities, and emotional intelligence. 

The goal of kinematic analysis is to provide objective metrics that enhance our ability to teach, learn, and perform at the highest level, not to reduce surgery to a series of measurements. Technology serves human expertise, not the other way around. 

Conclusion 

Integrating kinematic data into surgical training marks a fundamental shift in how surgical skill is conceptualized, taught, and evaluated. These objective performance metrics go beyond video review to quantify surgical movement quality, enabling more evidence-based training and assessment. As these technologies evolve, they hold immense potential to accelerate technical skill acquisition and improve patient outcomes. By redefining how we measure mastery, kinematic data has the power to transform surgical education and help build a safer, more proficient surgical workforce. 

Recommended Reading 

1. Cedars Sinai. (n.d.). Hung Lab. Health Sciences University. https://www.cedars-sinai.edu/health-sciences-university/research/labs/hung.html  

2. Shaharan, S., Ryan, D.M, & Neary, P.C. (2017). Motion Tracking System in Surgical Training. IntechOpen. https://doi.org/10.5772/intechopen.68850  

3. Villarreal, J. (2024, November 4). Surgical Video Review: A Gold Mine for New Residents and Fellows [blog]. Surgical Safety Technologies. https://www.surgicalsafety.com/blog/surgical-video-review-gold-mine-for-new-residents-fellows  

4. Azari, D.P., Frasier, L.L., Quamme, S.R.P., et., al. (2020). Modeling Surgical Technical Skill Using Expert Assessment for Automated Computer Rating. Ann Surg.;269(3):574-581. https://doi.org/10.1097/SLA.0000000000002478  

5. Pugh, C.M. (2023). The Quantified Surgeon: A Glimpse Into the Future of Surgical Metrics and Outcomes. The American Surgeon™;89(9):3691-3694. https://www.doi.org/10.1177/00031348231168315  

6. Villarreal, J. (2025, April 16). Redefining Surgical Training: Harnessing Technology for Resident Growth [blog]. Surgical Safety Technologies. https://www.surgicalsafety.com/blog/redefining-surgical-training-for-resident-growth  

7. Hung, A. (2021). SIU 2021: The Quantified Surgeon: Predicting Patient Outcomes after Robotic Surgery and Automating Skills Assessment. UroToday.com. https://www.urotoday.com/conference-highlights/siu-2021/133939-siu-2021-the-quantified-surgeon-predicting-patient-outcomes-after-robotic-surgery-and-automating-skills-assessment. Accessed April 20, 2025. 

8. Wile, R.K., Brian, R., Rodriguez, N., et., al. (2023). Home practice for robotic surgery: a randomized controlled trial of a low-cost simulation model. J Robot Surg.;17(5):2527-2536. https://doi.org/10.1007/s11701-023-01688-7  

9. Loukas, C., & Prevezanou, K. (2025). Assessment of Training Progression on a Surgical Simulator Using Machine Learning and Explainable Artificial Intelligence Techniques. Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - ICPRAM; SciTePress, 465-474. https://doi.org/10.5220/0013109500003905  

10. Ershad, M., Rege, R., & Fey, A.M. (2021). Adaptive Surgical Robotic Training Using Real-Time Stylistic Behavior Feedback Through Haptic Cues. IEEE Transactions on Medical Robotics and Bionics;3(4), 959-969. https://doi.org/10.1109/TMRB.2021.3124128  

11. Fard, M.J., Ameri, S., & Ellis, R.D. (2016). Toward Personalized Training and Skill Assessment in Robotic Minimally Invasive Surgery. Proceedings of the World Congress on Engineering and Computer Science 2016. https://doi.org/10.48550/arXiv.1610.07245 

12. Hung, A.J., Rambhatla, S., Sanford, D.I., et., al. (2022). Road to automating robotic suturing skills assessment: Battling mislabeling of the ground truth. Surgery; 171(4):915-919. https://doi.org/10.1016/j.surg.2021.08.014  

13. Lam, K., Chen, J., Wang, Z., et., al. (2022). Machine learning for technical skill assessment in surgery: a systematic review. npj Digit. Med.;5(24). https://doi.org/10.1038/s41746-022-00566-0