Surgical Endoscopy

, Volume 32, Issue 7, pp 3096–3107 | Cite as

Interpretation of motion analysis of laparoscopic instruments based on principal component analysis in box trainer settings

  • Ignacio OropesaEmail author
  • Fernando Pérez Escamirosa
  • Juan A. Sánchez-Margallo
  • Silvia Enciso
  • Borja Rodríguez-Vila
  • Arturo Minor Martínez
  • Francisco M. Sánchez-Margallo
  • Enrique J. Gómez
  • Patricia Sánchez-González



Motion analysis parameters (MAPs) have been extensively validated for assessment of minimally invasive surgical skills. However, there are discrepancies on how specific MAPs, tasks, and skills match with each other, reflecting that motion analysis cannot be generalized independently of the learning outcomes of a task. Additionally, there is a lack of knowledge on the meaning of motion analysis in terms of surgical skills, making difficult the provision of meaningful, didactic feedback. In this study, new higher significance MAPs (HSMAPs) are proposed, validated, and discussed for the assessment of technical skills in box trainers, based on principal component analysis (PCA).


Motion analysis data were collected from 25 volunteers performing three box trainer tasks (peg grasping/PG, pattern cutting/PC, knot suturing/KS) using the EVA tracking system. PCA was applied on 10 MAPs for each task and hand. Principal components were trimmed to those accounting for an explained variance > 80% to define the HSMAPs. Individual contributions of MAPs to HSMAPs were obtained by loading analysis and varimax rotation. Construct validity of the new HSMAPs was carried out at two levels of experience based on number of surgeries.


Three new HSMAPs per hand were defined for PG and PC tasks, and two per hand for KS task. PG presented validity for HSMAPs related to insecurity and economy of space. PC showed validity for HSMAPs related to cutting efficacy, peripheral unawareness, and confidence. Finally, KS presented validity for HSMAPs related with economy of space and knotting security.


PCA-defined HSMAPs can be used for technical skills’ assessment. Construct validation and expert knowledge can be combined to infer how competences are acquired in box trainer tasks. These findings can be exploited to provide residents with meaningful feedback on performance. Future works will compare the new HSMAPs with valid scoring systems such as GOALS.


Box trainer Motion analysis EVA tracking system Principal component analysis HSMAP 



The authors would like to acknowledge all staff of the Jesús Usón Minimally Invasive Surgery Centre involved in setting up and performing the experiments described in this work.

Compliance with ethical standards


Drs. I. Oropesa, F. Pérez Escamirosa, J.A. Sánchez Margallo, S. Enciso, B. Rodríguez-Vila, A. Minor Martínez, F.M Sánchez-Margallo, P. Sánchez-González, and E.J. Gómez have no conflict of interests or financial ties to disclose.


  1. 1.
    Sánchez-Margallo JA, Sánchez-Margallo FM, Oropesa I, Enciso S, Gómez EJ (2017) Objective assessment based on motion-related metrics and technical performance in laparoscopic suturing. Int J Comput Assist Radiol Surg 12:307–314. CrossRefPubMedGoogle Scholar
  2. 2.
    van Hove PD, Tuijthof GJM, Verdaasdonk EGG, Stassen LPS, Dankelman J (2010) Objective assessment of technical surgical skills. Br J Surg 97:972–987. CrossRefPubMedGoogle Scholar
  3. 3.
    Partridge RW, Brown FS, Brennan PM, Hennessey IAM, Hughes MA (2016) The LEAPTM gesture interface device and take-home laparoscopic simulators. Surg Innov 23:70–77. CrossRefPubMedGoogle Scholar
  4. 4.
    Oropesa I, Sánchez-González P, Chmarra MK, Lamata P, Fernández Á, Sánchez-Margallo JA, Jansen FW, Dankelman J, Sánchez-Margallo FM, Gómez EJ (2013) EVA: laparoscopic instrument tracking based on endoscopic video analysis for psychomotor skills assessment. Surg Endosc 27:1029–1039. CrossRefPubMedGoogle Scholar
  5. 5.
    Escamirosa FP, Flores RMO, Oropesa I, Vidal CRZ, Martínez AM (2015) Face, content, and construct validity of the EndoViS training system for objective assessment of psychomotor skills of laparoscopic surgeons. Surg Endosc 29:3392–3403. CrossRefPubMedGoogle Scholar
  6. 6.
    Hofstad EF, Våpenstad C, Chmarra MK, Langø T, Kuhry E, Mårvik R (2013) A study of psychomotor skills in minimally invasive surgery: what differentiates expert and nonexpert performance. Surg Endosc 27:854–863. CrossRefPubMedGoogle Scholar
  7. 7.
    Oropesa I, Sánchez-González P, Lamata P, Chmarra MK, Pagador JB, Sánchez-Margallo JA, Sánchez-Margallo FM, Gómez EJ (2011) Methods and tools for objective assessment of psychomotor skills in laparoscopic surgery. J Surg Res 171:e81–e95. CrossRefPubMedGoogle Scholar
  8. 8.
    Feldman LS, Sherman V, Fried GM (2004) Using simulators to assess laparoscopic competence: ready for widespread use? Surgery 135:28–42. CrossRefPubMedGoogle Scholar
  9. 9.
    Fried GM, Feldman LS (2008) Objective assessment of technical performance. World J Surg 32:156–160. CrossRefPubMedGoogle Scholar
  10. 10.
    Vassiliou MC, Feldman LS, Andrew CG, Bergman S, Leffondré K, Stanbridge D, Fried GM (2005) A global assessment tool for evaluation of intraoperative laparoscopic skills. Am J Surg 190:107–113. CrossRefPubMedGoogle Scholar
  11. 11.
    Chang J, Banaszek DC, Gambrel J, Bardana D (2016) Global rating scales and motion analysis are valid proficiency metrics in virtual and benchtop knee arthroscopy simulators. Clin Orthop Relat Res 474:956–964. CrossRefPubMedGoogle Scholar
  12. 12.
    Cristancho SM, Hodgson AJ, Panton ONM, Meneghetti A, Warnock G, Qayumi K (2009) Intraoperative monitoring of laparoscopic skill development based on quantitative measures. Surg Endosc 23:2181–2190. CrossRefPubMedGoogle Scholar
  13. 13.
    Chmarra MK, Klein S, De Winter JCF, Jansen FW, Dankelman J (2010) Objective classification of residents based on their psychomotor laparoscopic skills. Surg Endosc 24:1031–1039. CrossRefPubMedGoogle Scholar
  14. 14.
    Oropesa I, Sánchez-González P, Chmarra MK, Lamata P, Pérez-Rodríguez R, Jansen FW, Dankelman J, Gómez EJ (2013) Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis. Surg Endosc 28:657–670. CrossRefGoogle Scholar
  15. 15.
    Horeman T, Dankelman J, Jansen FW, van den Dobbelsteen JJ (2014) Assessment of laparoscopic skills based on force and motion parameters. IEEE Trans Biomed Eng 61:805–813. CrossRefPubMedGoogle Scholar
  16. 16.
    Enciso Sanz S, Sánchez Margallo FM, Díaz-Güemes Martín-Portugués I, Usón Gargallo J (2012) Preliminary validation of the Simulap(®) physical simulator and its assessment system for laparoscopic surgery. Cir Esp 90:38–44. CrossRefPubMedGoogle Scholar
  17. 17.
    Abdi HE, Williams LJ (2010) Principal component analysis. WIREs Comp Stat 2:433–459. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ignacio Oropesa
    • 1
    return OK on get
  • Fernando Pérez Escamirosa
    • 2
  • Juan A. Sánchez-Margallo
    • 3
  • Silvia Enciso
    • 4
  • Borja Rodríguez-Vila
    • 1
    • 5
  • Arturo Minor Martínez
    • 6
  • Francisco M. Sánchez-Margallo
    • 3
  • Enrique J. Gómez
    • 1
    • 5
  • Patricia Sánchez-González
    • 1
    • 5
  1. 1.Biomedical Engineering and Telemedicine Centre (GBT), ETSI Telecomunicación, Center for Biomedical TechnologyUniversidad Politécnica de Madrid (UPM)MadridSpain
  2. 2.Department of Surgery, Faculty of MedicineUniversidad Nacional Autónoma de México (UNAM)Mexico CityMexico
  3. 3.Bioengineering and Health Technologies UnitJesús Usón Minimally Invasive Surgery CentreCáceresSpain
  4. 4.Laparoscopy Unit Jesús Usón Minimally Invasive Surgery CentreCáceresSpain
  5. 5.Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)MadridSpain
  6. 6.Department of Electrical Engineering, Bioelectronics SectionCentro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV–IPN)Mexico CityMexico

Personalised recommendations