1. Introduction Hand motion analysis through specific devices has been successfully used for years in the surgical field. (1) More recently, they have been used in anesthesia as assessment tools for procedural skills.

2. Motion-Tracking Technology Motion-tracking devices may be divided into two forms: optical and nonoptical.

- Optical systems typically use high-speed cameras to detect either infrared light reflection or emission, from which three-dimensional positional data can be extracted and postprocessed.

- Nonoptical systems typically rely on one of three methods of data acquisition to determine orientation and movement: electromagnetic, mechanical, and inertial mechanisms.

-Two main different devices using electromagnetic fields have been described in the anesthesia literature¹:

1.- The Imperial College Surgical Assessment Device (ICSAD) is a device that tracks operator’s hand-motion. It uses an electromagnetic tracking system (Isotrak Il; Polhemus Inc., Colchester, VT, USA) consisting of an electromagnetic field generator and sensors placed on the back of the operator’s hands. Three dexterity scores can be measured: total distance travelled by each hand, number of movements, and total time.

2.- The HMA hardware consisted of a DriveBay electromagnetic field generator and control box (Ascension, VT, USA), one reference sensor, and two hand sensors (Model 800, 7.9 mm, 6-DOF). Three-dimensional position data from the electromagnetic sensors are registered using an open -source software. Metrics used to evaluate motion efficiency are the same: total time of procedure, total path length (distance travelled) and number of translational motions. Both systems collect the x, y, z Cartesian coordinate information from each sensor at a determined resolution and frequency. Most reports of ICSAD use an accuracy of 1mm at 20 Hz. On the other hand, DriveBay device reports an accuracy of 1.4 mm at 50 Hz.

Finally, the use of this motion device in the evaluation of motor skills allows obtaining quantitative data complementing previous validated visual scales. Having as many instruments as possible for evaluating motor skills could improve the learning process. In the future, if we want to set up metrics or cutoff scores to be achieved with motor skills training, a previous standardization of both parameters to be used and calibration thresholds should be established for each setting.

3. Value of Motion Metrics:

The ICSAD has demonstrated construct validity in many surgical procedures, including open, laparoscopic, and microsurgery. Additionally, in the anesthesia field, its construct and concurrent validity has been established in labor epidural placement, spinal anesthesia, ultrasound-guided supraclavicular block, and jugular CVC placement.^3-7

The DriveBay device was validated because motion parameters discriminate between expert and novices and correlates to a previously published modified GRS.⁸

4. Limitations to Motion-Tracking Technology:

Three dexterity scores can be measured: total distance travelled by each hand, number of movements, and total time. The number of hand movements is determined based on a calibration process of translational and rotational velocity thresholds. Therefore, the number of movements registered is highly dependent upon the thresholds the researchers have pre-defined. Clearly, evidence supports that tracking motion devices are valid assessment tools for procedural skills. Nevertheless, given those technical calibration processes, carefully interpretation should be taken in consideration while extrapolating this type of data.¹

5. Potential applications of motion tracking.

Today motion analysis could be used for providing objective feedback in training, debriefing after procedures, and evaluating clinical competence.⁹

Nowadays these types of sensors are not used regularly in the operating room, as a guidance to perform peripheral nerve blocks.

But is there the possibility of motion analysis application for performance assessment of clinical procedures on actual patients?

Is there space for this technology (motion sensors and artificial intelligence) to find subtle patterns, biomechanical traces, and kinetic characteristics of expert performance, to guide the performance of peripheral nerve blocks?

Previous investigations have used the expert performance approach, described by Ericsson, to evaluate patterns as indicators of performance, as characteristics of an expert execution in contrast with de performance of an inexperienced operator.¹⁰

In the surgical field there is a lot of information generated around the use of artificial intelligence to evaluate performance of gesture in surgical procedures: a systematic review published in 2022 collects at least 66 articles on the topic, identifying the most used methodologies, current limitations, and future challenges.¹¹

Another innovative idea is needle tip tracking. Kåsine reported that needle tip tracking did not reduce procedural time for out-of-plane ultrasound-guided lumbar plexus block but did reduce the number of hand movements and path lengths.¹²

References

1. Aggarwal R, Dosis A, Bello F, Darzi A: Motion tracking systems for assessment of surgical skill. Surg Endosc 2007;21(2):339.

2. Corvetto MA, Altermatt FR. Tracking Motion Devices as Assessment Tools in Anesthesia Procedures: Have We Been Using Them Well? CJEM. 2017 Sep;19(5):412-413.

3. Hayter MA, Friedman Z, Bould MD, Hanlon JG, Katznelson R, Borges B, Naik VN: Validation of the Imperial College Surgical Assessment Device (ICSAD) for labour epidural placement. Can J Anaesth 2009;56(6):419-426.

4. Chin KJ, Tse C, Chan V, Tan JS, Lupu CM, Hayter M: Hand motion analysis using the imperial college surgical assessment device: validation of a novel and objective performance measure in ultrasound-guided peripheral nerve blockade. Reg Anesth Pain Med 2011;36(3):213-219.

5. Varas J, Achurra P, Leon F, Castillo R, De La Fuente N, Aggarwal R, Clede L, Bravo MP, Corvetto M, Montana R: Assessment of central venous catheterization in a simulated model using a motion-tracking device: an experimental validation study. Annals of surgical innovation and research 2016;10:2.

6. Clinkard D, Holden M, Ungi T, Messenger D, Davison C, Fichtinger G, McGraw R: The development and validation of hand motion analysis to evaluate competency in central line catheterization. Acad Emerg Med 2015;22(2):212-218.

7. Corvetto MA, Fuentes C, Araneda A, Achurra P, Miranda P, Viviani P, Altermatt FR. Validation of the imperial college surgical assessment device for spinal anesthesia. BMC Anesthesiol. 2017 Sep 29;17(1):131

8. McGraw R, Chaplin T, McKaigney C, Rang L, Jaeger M, Redfearn D, Davison C, Ungi T, Holden M, Yeo C, et al: Development and Evaluation of a Simulation-based Curriculum for Ultrasound-guided Central Venous Catheterization. Cjem 2016;18(6):405-413.

9. Baribeau V, Weinstein J, Wong VT, Sharkey A, Lodico DN, Matyal R, Mahmood F, Mitchell JD. Motion-Tracking Machines and Sensors: Advancing Education Technology. J Cardiothorac Vasc Anesth. 2022 Jan;36(1):303-30810)

10. Altermatt FR, Corvetto MA. Analizando el desempeño de expertos para definir patrones de excelencia en destrezas procedurales. Simulación Clínica. 2022;4(3):101-105. doi:10.35366/109710.

11. Lam K, Cheng J, Wang Z, Iqbal FM, Darzi A, Lo B, et al. Machine learning for technical skill assessment in surgery: a systematic review. Npj Digital Medicine. 2022;5:24.

12. Kåsine T, Romundstad L, Rosseland LA, Ullensvang K, Fagerland MW, Kessler P, Bjørnå E, Sauter AR. The effect of needle tip tracking on procedural time of ultrasound-guided lumbar plexus block: a randomised controlled trial. Anaesthesia. 2020 Jan;75(1):72-79.