Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills.

Background: A trainer for online laparoscopic surgical skills assessment based on the performance of experts and nonexperts is presented. The system uses computer vision, augmented reality, and artificial intelligence algorithms, implemented into a Raspberry Pi board with Python programming language.

Methods: Two training tasks were evaluated by the laparoscopic system: transferring and pattern cutting.

Evaluation of suitability of a micro-processing unit of motion analysis for upper limb tracking.

The aim of this study is to assess the suitability of a micro-processing unit of motion analysis (MPUMA), for monitoring, reproducing, and tracking upper limb movements. The MPUMA is based on an inertial measurement unit, a 16-bit digital signal controller and a customized algorithm. To validate the performance of the system, simultaneous recordings of the angular trajectory were performed with a video-based motion analysis system.

Analysis of Electromyographic Signals from Rats' Stomaches for Detection and Classification of Motility.

This paper presents the analysis of the electromyographic signals from rat stomaches to identify and classify contractions. The results were validated with both visual identification and an ultrasonic system to guarantee the reference. Some parameters were defined and associated to the energy of the signal in frequency domain and grouped in a P vector.