In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 6 ( 2022-6-3), p. e0267936-
Abstract:
Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons’ differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot tying could be determined using wearable EMG and accelerometer sensors. A wireless wearable platform has made it feasible to collect movement and muscle activation signals for quick skill evaluation during surgical tasks. However, it is challenging since the placement of multiple wireless wearable sensors may interfere with their performance in the assessment. This study utilizes machine learning techniques to identify optimal muscles and features critical for accurate skill evaluation. This study enrolled a total of twenty-six surgeons of different skill levels: novice (n = 11), intermediaries (n = 12), and experts (n = 3). Twelve wireless wearable sensors consisting of surface EMGs and accelerometers were placed bilaterally on bicep brachii, tricep brachii, anterior deltoid, flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU), and thenar eminence (TE) muscles to assess muscle activations and movement variability profiles. We found features related to movement complexity such as approximate entropy, sample entropy, and multiscale entropy played a critical role in skill level identification. We found that skill level was classified with highest accuracy by i) ECU for Random Forest Classifier (RFC), ii) deltoid for Support Vector Machines (SVM) and iii) biceps for Naïve Bayes Classifier with classification accuracies 61%, 57% and 47%. We found RFC classifier performed best with highest classification accuracy when muscles are combined i) ECU and deltoid (58%), ii) ECU and biceps (53%), and iii) ECU, biceps and deltoid (52%). Our findings suggest that quick surgical skill evaluation is possible using wearables sensors, and features from ECU, deltoid, and biceps muscles contribute an important role in surgical skill evaluation.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0267936
DOI:
10.1371/journal.pone.0267936.g001
DOI:
10.1371/journal.pone.0267936.g002
DOI:
10.1371/journal.pone.0267936.g003
DOI:
10.1371/journal.pone.0267936.g004
DOI:
10.1371/journal.pone.0267936.g005
DOI:
10.1371/journal.pone.0267936.g006
DOI:
10.1371/journal.pone.0267936.g007
DOI:
10.1371/journal.pone.0267936.g008
DOI:
10.1371/journal.pone.0267936.g009
DOI:
10.1371/journal.pone.0267936.g010
DOI:
10.1371/journal.pone.0267936.g011
DOI:
10.1371/journal.pone.0267936.g012
DOI:
10.1371/journal.pone.0267936.g013
DOI:
10.1371/journal.pone.0267936.g014
DOI:
10.1371/journal.pone.0267936.g015
DOI:
10.1371/journal.pone.0267936.t001
DOI:
10.1371/journal.pone.0267936.t002
DOI:
10.1371/journal.pone.0267936.t003
DOI:
10.1371/journal.pone.0267936.t004
DOI:
10.1371/journal.pone.0267936.t005
DOI:
10.1371/journal.pone.0267936.s001
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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