Surgical skills: Can learning curves be computed from recordings of surgical activities?

Forestier, Germain; Riffaud, Laurent; Petitjean, François; Henaux, Pierre Louis; Jannin, Pierre

doi:10.1007/s11548-018-1713-y

Cited by 20 publications

(17 citation statements)

References 44 publications

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“…They also showed how their proposed approach [33] allows the use of both kinematic and video data. However, studies in [34,18,35] showed that feedback on medical practice allows surgeons to improve their performance and achieve even higher skill levels. Hence the main drawback of the previous approaches is the difficulty for the trainees to understand the output and to use it as a feedback to improve their performance.…”

Section: Related Workmentioning

confidence: 99%

Surgical motion analysis using discriminative interpretable patterns

Forestier

Petitjean

Senin

et al. 2018

Artificial Intelligence in Medicine

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Section: Related Workmentioning

confidence: 99%

Surgical motion analysis using discriminative interpretable patterns

Forestier

Petitjean

Senin

et al. 2018

Artificial Intelligence in Medicine

Self Cite

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“…Research on motor sequence 11 – 13 , implicit 14 , 15 , and statistical learning 16 has provided evidence of underlying neural and cognitive mechanisms, but has generally been limited to highly artificial tasks such as executing invariant key-press sequences or recognizing simple artificial grammars of known structure. This limits our ability to generalize findings to understand the learning and execution of the real-world skills 17 , 18 of interest to fields ranging from sport science 19 and surgery 20 , to human behavioral ecology 21 , and comparative psychology 22 .…”

Section: Introductionmentioning

confidence: 99%

The measurement, evolution, and neural representation of action grammars of human behavior

Stout

Chaminade

Apel

et al. 2021

Sci Rep

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Human behaviors from toolmaking to language are thought to rely on a uniquely evolved capacity for hierarchical action sequencing. Testing this idea will require objective, generalizable methods for measuring the structural complexity of real-world behavior. Here we present a data-driven approach for extracting action grammars from basic ethograms, exemplified with respect to the evolutionarily relevant behavior of stone toolmaking. We analyzed sequences from the experimental replication of ~ 2.5 Mya Oldowan vs. ~ 0.5 Mya Acheulean tools, finding that, while using the same “alphabet” of elementary actions, Acheulean sequences are quantifiably more complex and Oldowan grammars are a subset of Acheulean grammars. We illustrate the utility of our complexity measures by re-analyzing data from an fMRI study of stone toolmaking to identify brain responses to structural complexity. Beyond specific implications regarding the co-evolution of language and technology, this exercise illustrates the general applicability of our method to investigate naturalistic human behavior and cognition.

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“…Research on motor sequence [11][12][13] , implicit 14,15 , and statistical learning 16 has provided evidence of underlying neural and cognitive mechanisms, but has generally been limited to highly artificial tasks such as executing invariant key-press sequences or recognizing simple artificial grammars of known structure. This limits our ability to generalize findings to understand the learning and execution of the real-world skills 17,18 of interest to fields ranging from sport science 19 and surgery 20 , to human behavioral ecology 21 , and comparative psychology 22 .…”

Section: Introductionmentioning

confidence: 99%

Action Grammars of Human Behavior – Measurement, Evolution, and Neural Representation

Stout¹,

Chaminade²,

Apel³

et al. 2021

Preprint

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Human behaviors from tool-making to language are thought to rely on a uniquely evolved capacity for hierarchical action sequencing. Testing this idea will require objective, generalizable methods for measuring the structural complexity of real-world behavior. Here we present a data-driven approach for extracting action grammars from basic ethograms, exemplified with respect to the evolutionarily-relevant behavior of stone tool-making. We analyzed sequences from the experimental replication of ~2.5 Mya Oldowan vs. ~0.5 Mya Acheulean tools, finding that, while using the same “alphabet” of elementary actions, Acheulean sequences are quantifiably more complex and Oldowan grammars are a subset of Acheulean grammars. We illustrate the utility of our complexity measures by re-analyzing data from an fMRI study of stone tool-making to identify brain responses to structural complexity. Beyond specific implications regarding the co-evolution of language and technology, this exercise illustrates the general applicability of our method to investigate naturalistic human behavior and cognition.

show abstract

Surgical skills: Can learning curves be computed from recordings of surgical activities?

Abstract: Preliminary results suggest that our approach constitutes a useful addition to surgical training monitoring.

Cited by 20 publications

References 44 publications

Surgical motion analysis using discriminative interpretable patterns

Surgical motion analysis using discriminative interpretable patterns

The measurement, evolution, and neural representation of action grammars of human behavior

Action Grammars of Human Behavior – Measurement, Evolution, and Neural Representation

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