Evidence map›Paper›PMID 31779237›Full record

ArticleSensors (Basel, Switzerland)2019

A Gesture Recognition Algorithm for Hand-Assisted Laparoscopic Surgery.

Carmen López-Casado, Enrique Bauzano, Irene Rivas-Blanco, Carlos J Pérez-Del-Pulgar, Víctor F Muñoz

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Carmen López-CasadoDepartment of Systems Engineering and Automation, Universidad de Málaga, Andalucía Tech, 29071 Málaga, Spain.ORCID 0000-0002-3583-051X
Enrique BauzanoDepartment of Systems Engineering and Automation, Universidad de Málaga, Andalucía Tech, 29071 Málaga, Spain.ORCID 0000-0002-0753-8714
Irene Rivas-BlancoDepartment of Systems Engineering and Automation, Universidad de Málaga, Andalucía Tech, 29071 Málaga, Spain.ORCID 0000-0002-6936-1918
Carlos J Pérez-Del-PulgarDepartment of Systems Engineering and Automation, Universidad de Málaga, Andalucía Tech, 29071 Málaga, Spain.ORCID 0000-0001-5819-8310
Víctor F MuñozDepartment of Systems Engineering and Automation, Universidad de Málaga, Andalucía Tech, 29071 Málaga, Spain.
Universidad de Málaga · ES

Funding

Ministerio de Economía y Competitividad (Spanish government) DPI2016-80391-C3-R
6 · The paper itself

Abstract

Minimally invasive surgery (MIS) techniques are growing in quantity and complexity to cover a wider range of interventions. More specifically, hand-assisted laparoscopic surgery (HALS) involves the use of one surgeon's hand inside the patient whereas the other one manages a single laparoscopic tool. In this scenario, those surgical procedures performed with an additional tool require the aid of an assistant. Furthermore, in the case of a human-robot assistant pairing a fluid communication is mandatory. This human-machine interaction must combine both explicit orders and implicit information from the surgical gestures. In this context, this paper focuses on the development of a hand gesture recognition system for HALS. The recognition is based on a hidden Markov model (HMM) algorithm with an improved automated training step, which can also learn during the online surgical procedure by means of a reinforcement learning process.

Indexed as

AlgorithmsGesturesHand-Assisted LaparoscopyHumansMinimally Invasive Surgical ProceduresPattern Recognition, AutomatedRoboticsgesture recognitionmachine learningsurgical robotics

Identifiers

PMID31779237
PMCPMC6929113
OpenAlexW2991129060

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.