Educational impact of hand motion analysis in the evaluation of fast examination skills

  • Mauro Zago
  • Chiarella Sforza
  • Diego Mariani
  • Matteo Marconi
  • Alan Biloslavo
  • Antonio La Greca
  • Hayato Kurihara
  • Andrea Casamassima
  • Samantha Bozzo
  • Francesco Caputo
  • Manuela Galli
  • Matteo ZagoEmail author
Original Article



Increasing pressure pushes towards the objective competence assessment of clinical operators. Hand motion analysis (HMA) was introduced to measure surgical and clinical procedures; its recent application to FAST examinations leaves unsolved issues. This study aimed at determining optimal HMA parameters to discriminate between operators’ skill levels, and which FAST tasks are experience-dependent.


Ten experienced (EG) and 13 beginner (BG) sonographers performed a FAST examination on one female and one male model. A motion capture system returned the duration, working volume, number of movements (absolute and time normalized), and hand path length (absolute and time normalized) of each view.


BG took more time in completing specific views, with a higher working volume (p = 0.003) and longer hands path (p < 0.001). The number of movements was lower in the EG (p < 0.001) and differed between views (p = 0.014). No significant Group/Model differences were found for the normalized number of movements. The LUQ view required a higher number of movements (p < 0.001).


HMA identified kinematic parameters discriminating between proficiency level and critical subtasks in the FAST examination. These findings could be the base for a focused HMA-based evaluation of performances following a proctored training period. There is room to incorporate HMA into simulation metrics and evidence-based credentialing standards for clinical ultrasound applications.


Skill assessment Ultrasound Competence FAST Initial assessment Management 



The author received no specific funding for this work.

Compliance with ethical standards

Conflict of interest

The authors have declared that no competing interests exist.


  1. 1.
    Dosis A, Aggarwal R, Bello F, Moorthy K, Munz Y, Gillies D, et al. Synchronized video and motion analysis for the assessment of procedures in the operating theater. Arch Surg. 2005;140:293–9.CrossRefGoogle Scholar
  2. 2.
    Glarner CE, Hu YY, Chen CH, Radwin RG, Zhao Q, Craven MW, et al. Quantifying technical skills during open operations using video-based motion analysis. Surgery. 2014;156:729–34.CrossRefGoogle Scholar
  3. 3.
    McGoldrick RB, Davis CR, Paro J, Hui K, Nguyen D, Lee GK. Motion analysis for microsurgical training: objective measures of dexterity, economy of movement, and ability. Plast Reconstr Surg. 2015;136:231e–240e.CrossRefGoogle Scholar
  4. 4.
    Grober ED, Roberts M, Shin EJ, Mahdi M, Bacal V. Intraoperative assessment of technical skills on live patients using economy of hand motion: establishing learning curves of surgical competence. Am J Surg. 2010;199:81–5.CrossRefGoogle Scholar
  5. 5.
    Sgouros NP, Loukas C, Koufi V, Troupis TG, Georgiou E. An automated skills assessment framework for laparoscopic training tasks. Int J Med Robot Comput Assist Surg. 2018;14:1–10.CrossRefGoogle Scholar
  6. 6.
    D’Angelo A-LD, Rutherford DN, Ray RD, Laufer S, Mason A, Pugh CM. Working volume: evaluating validity evidence of a new measure of surgical efficiency. Am J Surg. 2016;211(2):445–50. CrossRefGoogle Scholar
  7. 7.
    Ziesmann MT, Park J, Unger B, Kirkpatrick AW, Vergis A, Pham C, et al. Validation of hand motion analysis as an objective assessment tool for the focused assessment with sonography for trauma examination. J Trauma Acute Care Surg. 2015;79:631–7.CrossRefGoogle Scholar
  8. 8.
    Zago M. Time for a comprehensive ultrasound-enhanced trauma management. Eur J Trauma Emerg Surg. 2009;35:339–40.CrossRefGoogle Scholar
  9. 9.
    Padalino P, Bomben F, Chiara O, Montagnolo G, Marini A, Zago M, et al. Healing of blunt liver Injury after non-operative management: role of ultrasonography follow-up. Eur J Trauma Emerg Surg. 2009;35:364–70.CrossRefGoogle Scholar
  10. 10.
    Jeanmonod R, Stawicki SP, Bahner DP, Zago M. Advancing clinician-performed sonography in the twenty-first century: building on the rich legacy of the twentieth century pioneers. Eur J Trauma Emerg Surg. 2016;42:115–8.CrossRefGoogle Scholar
  11. 11.
    Sahlani L, Thompson L, Vira A, Panchal AR. Bedside ultrasound procedures: musculoskeletal and non-musculoskeletal. Eur J Trauma Emerg Surg. 2016;42:127–8.CrossRefGoogle Scholar
  12. 12.
    Lewis GC, Crapo SA, Williams JG. Critical skills and procedures in emergency medicine. Vascular access skills and procedures. Emerg Med Clin N Am. 2013;31:59–86.CrossRefGoogle Scholar
  13. 13.
    Ratnasekera A, Ferrada P. Ultrasonographic-guided resuscitation of the surgical patient. JAMA Surg. 2018;153:77–8.CrossRefGoogle Scholar
  14. 14.
    Pereira J, Afonso AC, Constantino J, Matos A, Henriques C, Zago M, et al. Accuracy of ultrasound in the diagnosis of acute cholecystitis with coexistent acute pancreatitis. Eur J Trauma Emerg Surg. 2017;43:79–83.CrossRefGoogle Scholar
  15. 15.
    Ziesmann MT, Park J, Unger BJ, Kirkpatrick AW, Vergis A, Logsetty S, et al. Validation of the quality of ultrasound imaging and competence (QUICk) score as an objective assessment tool for the FAST examination. J Trauma Acute Care Surg. 2015;78:1008–13.CrossRefGoogle Scholar
  16. 16.
    Zago M, Martinez Casas I, Pereira J, Mariani D, Silva AR, Casamassima A, et al. Tailored ultrasound learning for acute care surgeons: a review of the MUSEC (Modular UltraSound ESTES Course) project. Eur J Trauma Emerg Surg. 2016;42:161–8.CrossRefGoogle Scholar
  17. 17.
    Annet M. Subgroup handedness and the probability of nonright preference for foot or eye and of a nonright-handed parent. Percept Mot Skills. 2001;93(3):911–4.CrossRefGoogle Scholar
  18. 18.
    Todsen T, Tolsgaard MG, Olsen BH, Henriksen BM, Hillingsø JG, Konge L, et al. Reliable and valid assessment of point-of-care ultrasonography. Ann Surg. 2015;261:309–15.CrossRefGoogle Scholar
  19. 19.
    Matyal R, Mitchell J, Hess P, Chaudary B, Bose R, Jainandunsin J, et al. Simulator-based transesophageal echocardiographic training with motion analysis: a curriculum-based approach. Anesthesiology. 2014;121:389–99.CrossRefGoogle Scholar
  20. 20.
    Clinkard D, Holden M, Ungi T, Messenger D, Davison C, Fichtinger G, et al. The development and validation of hand motion analysis to evaluate competency in central line catheterization. Acad Emerg Med. 2015;22:212–8.CrossRefGoogle Scholar
  21. 21.
    Chmarra MK, Klein S, De Winter JCF, Jansen FW, Dankelman J. Objective classification of residents based on their psychomotor laparoscopic skills. Surg Endosc Other Interv Tech. 2010;24:1031–9.CrossRefGoogle Scholar
  22. 22.
    Datta V, Mackay S, Mandalia M, Darzi A. The use of electromagnetic motion tracking analysis to objectively measure open surgical skill in the laboratory-based model. J Am Coll Surg. 2001;193:479–85.CrossRefGoogle Scholar
  23. 23.
    Hassan EA, Jenkyn TR, Dunning CE. Direct comparison of kinematic data collected using an electromagnetic tracking system versus a digital optical system. J Biomech. 2007;40:930–5.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Surgery, Minimally Invasive Surgery UnitPoliclinico San PietroBergamoItaly
  2. 2.Department of Biomedical Sciences for HealthUniversità degli Studi di MilanoMilanItaly
  3. 3.Institute of Molecular Bioimaging and PhysiologyNational Research CouncilSegrateItaly
  4. 4.General Surgery DepartmentLegnano Hospital, ASST Ovest MilaneseLegnanoItaly
  5. 5.General Surgery DepartmentS. Maria delle Stelle Hospital, ASST Melegnano e MartesanaMilanItaly
  6. 6.General Surgery DepartmentCattinara University HospitalTriesteItaly
  7. 7.Department of Surgery, Emergency Surgery Unit, Policlinico GemelliCatholic University of the Sacred HeartRomeItaly
  8. 8.Emergency and Trauma Surgery UnitHumanitas Research HospitalRozzanoItaly
  9. 9.General Surgery DepartmentS. Maria delle Stelle Hospital, ASST Melegnano e Martesana MelzoCernusco sul NaviglioItaly
  10. 10.Department of Electronics, Information and Bioengineering (DEIB)Politecnico di MilanoMilanItaly
  11. 11.Fondazione Istituto Farmacologico Filippo SerperoMilanItaly

Personalised recommendations