A smartwatch-based motion tracking application for the assessment of laparoscopic technical skills

Vivian E de Ruijter, MD, Genki Sugimoto, MSc, Sylvia Bereknyei, PhD, Laura M Mazer, MD, Edward S Shipper, MD, Dana T Lin, MD, FACS, James N Lau, MD, FACS. Stanford University

Objective of the technology:

The objective of this study is to assess laparoscopic psychomotor skills  using a novel smartwatch motion-tracking application.

Description of the technology:

Smartwatches are growing in popularity and availability, and show great promise for healthcare applications. Personal health and activity tracking have been the most obvious and immediate commercialization focus due to the development and cost reduction in accelerometer and gyroscope sensing technologies. However, smartwatch technology has the potential to transcend personal health accessories, by improving technical surgical skills. Manual dexterity assessment has historically been difficult due to the subjective nature of the apprenticeship model used in traditional clinical training. Current laparoscopic dexterity analysis technologies are either expensive, stationary, or have not been widely utilized outside the laboratory setting. In contrast, smartwatches are affordable, accessible, and mobile, thereby allowing continuous data collection on the go. Results of such real-time data could help a trainee to understand their progress in technical skills and set future goals. In this way, there may be an opportunity for this emerging technology to optimize technical skill training and evaluation of residents. In this study, we present the preliminary results on the development of a novel smartwatch motion-tracking application for laparoscopic psychomotor skills. The primary intent of this study was to 1) perform a proof-of-concept feasibility study and 2) determine if the data collected and analyzed by the application is able to discriminate between technical skill levels with acceleration data.

Methods:

Four subjects were recruited and assigned to two groups: novices (2 non-medical post-doctorals with no previous experience in surgery), and experts (2 board certified laparoscopic surgeons). Two Pebble Time smartwatches (Pebble©; Redwood City, CA) containing a 3-axis accelerometer (x, y, z) were attached to both wrists of study participants. Wrist motion data (time (s), path length (m), and speed (m/s) from both hands of study subjects were recorded while performing the Fundamentals of Laparoscopic Surgery (FLS) Peg Transfer Task. All recorded data was sent to a dedicated iOS smartphone application for instant review and storage of the data. For statistical analysis the independent t-test was used.

Preliminary results:

Both groups performed the FLS Peg Transfer Task 23 times (total n=46). The study showed that there was a significant difference between the expert and novice groups in: time (p<0.001), path length (left hand: p<0.001; right hand: p<0,001), and speed (p<0.001). Subject enrollment, data acquisition and data analysis are currently ongoing and expected to be completed in early spring of 2016.

Conclusions:

Commercially available wearable motion capture technologies, such as smartwatches, fitbits® etc., provide new opportunities for applications in other fields. This study provides preliminary data of a novel smartwatch motion-tracking app for the recording and quantification of manual dexterity for laparoscopic technical skills. In addition, we successfully demonstrate that this novel application for smartwatches is able to distinguish between motion patterns of experts and novices. Further studies are ongoing to optimize the software and to further validate the technology.