This paper presents a methodology for movement recognition in hand-assisted laparoscopic surgery using a textile-based sensing glove. The aim is to recognize the commands given by the surgeon's hand inside the patient's abdominal cavity in order to guide a collaborative robot. The glove, which incorporates piezoresistive sensors, continuously captures the degree of flexion of the surgeon's fingers. These data are analyzed throughout the surgical operation using an algorithm that detects and recognizes some defined movements as commands for the collaborative robot. However, hand movement recognition is not an easy task, because of the high variability in the motion patterns of different people and situations. The data detected by the sensing glove are analyzed using the following methodology. First, the patterns of the different selected movements are defined. Then, the parameters of the movements for each person are extracted. The parameters concerning bending speed and execution time of the movements are modeled in a prephase, in which all of the necessary information is extracted for subsequent detection during the execution of the motion. The results obtained with 10 different volunteers show a high degree of precision and recall.
This paper presents a system developed for the assistance with a collaborative robot in 12 hand-assisted laparoscopic surgery (HALS). The system includes a sensing glove with 13 piezoresistive sensors which capture continuously the flexion degree of the surgeon's fingers.14 These data are analyzed using an algorithm that detects and recognize the selected movements.
15This information is sent as commands to the collaborative robot throughout the surgical operation.
16The bending patterns, speed and execution times of the movements are modelled in a pre-phase in 17 which it will extract all the necessary information for later detection during the motion execution.
18The results obtained with 10 different volunteers show a high degree of accuracy and a low false 19 discovery rate.
20
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.