Estimation of Trunk Muscle Forces Using a Bio-Inspired Control Strategy Implemented in a Neuro-Osteo-Ligamentous Finite Element Model of the Lumbar Spine.

Low back pain (LBP), the leading cause of disability worldwide, remains one of the most common and challenging problems in occupational musculoskeletal disorders. The effective assessment of LBP injury risk, and the design of appropriate treatment modalities and rehabilitation protocols, require accurate estimation of the mechanical spinal loads during different activities. This study aimed to: (1) develop a novel 2D beam-column finite element control-based model of the lumbar spine and compare its predictions for muscle forces and spinal loads to those resulting from a geometrically matched equilibrium-based model; (2) test, using the foregoing control-based finite element model, the validity of the follower load (FL) concept suggested in the geometrically matched model; and (3) investigate the effect of change in the magnitude of the external load on trunk muscle activation patterns.

A validation study of a virtual-based haptic system for endoscopic sinus surgery training.

Background: The development of endoscopic sinus surgery (ESS) training simulators for clinical environment applications has reduced the existing shortcomings in conventional teaching methods, creating a standard environment for trainers and trainees in a more accurate and repeatable fashion.

Materials And Methods: In this research, the validation study of an ESS training simulator has been addressed. It is important to consider components that guide trainees to improve their hand movements control in the orbital floor removal in an ESS operation.

Phenomenological tissue fracture modeling for an Endoscopic Sinus and Skull Base Surgery training system based on experimental data.

The ideal simulator for Endoscopic Sinus and Skull Base Surgery (ESSS) training must be supported by a physical model and provide repetitive behavior in a controlled environment. Development of realistic tissue models is a key part of ESSS virtual reality (VR)-based surgical simulation. Considerable research has been conducted to address haptic or force feedback and propose a phenomenological tissue fracture model for sino-nasal tissue during surgical tool indentation.

A comprehensive multimodality heart motion prediction algorithm for robotic-assisted beating heart surgery.

Background: An essential requirement for performing robotic-assisted surgery on a freely beating heart is a prediction algorithm that can estimate the future heart trajectory.

Method: Heart motion, respiratory volume (RV) and electrocardiogram (ECG) signal were measured from two dogs during thoracotomy surgery. A comprehensive multimodality prediction algorithm was developed based on the multivariate autoregressive model to incorporate the heart trajectory and cardiorespiratory data with multiple inherent measurement rates explicitly.

A Hybrid Algorithm for Prediction of Varying Heart Rate Motion in Computer-Assisted Beating Heart Surgery.

An essential requirement for performing robotic assisted surgery on a freely beating heart is a prediction algorithm which can estimate the future trajectory of the heart in the varying heart rate (HR) conditions of real surgery with a high accuracy. In this study, a hybrid amplitude modulation- (AM) and autoregressive- (AR) based algorithm was developed to enable estimating the global and local oscillations of the beating heart, raised from its major and minor physiological activities. The AM model was equipped with an estimator of the heartbeat frequency to compensate for the HR variations.