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 simple 2D continuous beam-column model of the human lumbar spine, incorporating five pairs of Hill's muscle models, was developed in the frontal plane. Bio-inspired fuzzy neuro-controllers were used to maintain a laterally bent posture under five different external loading conditions. Muscle forces were assigned based on minimizing the kinematic error between target and actual postures, while imposing a penalty on muscular activation levels. As compared to the geometrically matched model, our control-based model predicted similar patterns for muscle forces, but at considerably lower values. Moreover, irrespective of the external loading conditions, a near (<3°) optimal FL on the spine was generated by the control-based predicted muscle forces. The variation of the muscle forces with the magnitude of the external load within the simulated range at the L1 level was found linear. This work presents a novel methodology, based on a bio-inspired control strategy, that can be used to estimate trunk muscle forces for various clinical and occupational applications toward shedding light on the ever-elusive LBP etiology.

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. Therefore, we defined three tasks to perform: pre-experiment learning, training, and evaluation. In these tasks, the critical regions introduced in the virtual training environment are forbidden to be touched. Recruiting 20 participants, divided into two groups, we investigated the performance metrics: quality (the percentage of the realism for the generated force for orbital floor removal and the usefulness of the proposed training system for the surgical educational curricula.), efficiency (time, path length), and safety (touching the goal and forbidden wall). RESULTS: All recruited participants answered a post-evaluation questionnaire regarding their perceptions of training system realism, potential educational benefits, and practiced skills. We investigate the differences between groups' performance metrics by utilizing the analysis of variance-Kruskal-Wallis test. Acquired results indicate that training before the actual process of the surgery has a significant effect on the accuracy and validity of the process for surgeons. CONCLUSIONS: Utilizing a standardized environment, trainers and trainees are able to carry out a process with regular features. In addition to traditional education methods, trainees can learn the risk of surgical operations. The training simulators can, also, provide a standard method for assessing the skills of surgical and medical students.

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. Mechanical properties of specific sino-nasal regions of the sheep head have been studied in various indentation and relaxation experiments. Tool insertion at different indentation rates into coronal orbital floor (COF) tissue is modeled as a sequence of three events: deformation, fracture, and cutting. The behavior in the deformation phase can be characterized using a non-linear, rate-dependent modified Kelvin-Voigt model. A non-linear model for tissue behavior prior to the fracture point is presented. The overall model shows a non-positive dependency of maximum force on tool indentation rate, which indicates faster tool insertion velocity decreases the maximum final fracture force. The tissue cutting phase has been modeled to characterize the force necessary to slice through the COF. The proposed model in this study can help develop VR-based ESSS base simulators in otolaryngology and ophthalmology surgeries. Such simulators are useful in preoperative planning, accurate surgical simulation, intelligent robotic assistance, and treatment applications.

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. RESULTS: Experimental results indicated strong relationships between the dominant frequencies of heart motion with RV and ECG. The prediction algorithm revealed a high steady state accuracy, with the root mean square (RMS) errors in the range of 82 to 162 µm for a 300-second interval, less than half of that of the best competitor. CONCLUSION: The proposed multimodality prediction algorithm is promising for practical use in robotic assisted beating heart surgery, considering its capability of providing highly accurate predictions in long horizons.

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. The RMS of the prediction errors of the hybrid algorithm was in the range of 165-361 µm for the varying HR motion, 21% less than that of the single AM model. With the capability of providing highly accurate predictions in a wide range of HR variation, the hybrid model is promising for practical use in robotic assisted beating heart surgery.

Arash: A social robot buddy to support children with cancer in a hospital environment.

This article presents the thorough design procedure, specifications, and performance of a mobile social robot friend Arash for educational and therapeutic involvement of children with cancer based on their interests and needs. Our research focuses on employing Arash in a pediatric hospital environment to entertain, assist, and educate children with cancer who suffer from physical pain caused by both the disease and its treatment process. Since cancer treatment causes emotional distress, which can reduce the efficiency of medications, using social robots to interact with children with cancer in a hospital environment could decrease this distress, thereby improving the effectiveness of their treatment. Arash is a 15 degree-of-freedom low-cost humanoid mobile robot buddy, carefully designed with appropriate measures and developed to interact with children ages 5-12 years old. The robot has five physical subsystems: the head, arms, torso, waist, and mobile-platform. The robot's final appearance is a significant novel concept; since it was selected based on a survey taken from 50 children with chronic diseases at three pediatric hospitals in Tehran, Iran. Founded on these measures and desires, Arash was designed, built, improved, and enhanced to operate successfully in pediatric cancer hospitals. Two experiments were devised to evaluate the children's level of acceptance and involvement with the robot, assess their feelings about it, and measure how much the robot was similar to the favored conceptual sketch. Both experiments were conducted in the form of storytelling and appearance/performance evaluations. The obtained results confirm high engagement and interest of pediatric cancer patients with the constructed robot.

Finite element modeling of trolling-mode AFM.

Trolling mode atomic force microscopy (TR-AFM) has overcome many imaging problems in liquid environments by considerably reducing the liquid-resonator interaction forces. The finite element model of the TR-AFM resonator considering the effects of fluid and nanoneedle flexibility is presented in this research, for the first time. The model is verified by ABAQUS software. The effect of installation angle of the microbeam relative to the horizon and the effect of fluid on the system behavior are investigated. Using the finite element model, frequency response curve of the system is obtained and validated around the frequency of the operating mode by the available experimental results, in air and liquid. The changes in the natural frequencies in the presence of liquid are studied. The effects of tip-sample interaction on the excitation of higher order modes of the system are also investigated in air and liquid environments.

Feasibility of infrared tracking of beating heart motion for robotic assisted beating heart surgery.

BACKGROUND: Accurate tracking of the heart surface motion is a major requirement for robot assisted beating heart surgery. METHOD: The feasibility of a stereo infrared tracking system for measuring the free beating heart motion was investigated by experiments on a heart motion simulator, as well as model surgery on a dog. RESULTS: Simulator experiments revealed a high tracking accuracy (81 µm root mean square error) when the capturing times were synchronized and the tracker pointed at the target from a 100 cm distance. The animal experiment revealed the applicability of the infrared tracker with passive markers in practical heart surgery conditions. CONCLUSION: With the current technology, infrared tracking with passive markers might be the optimal solution for accurate, fast, and reliable tracking of heart motion during robot assisted beating heart surgery.

Dynamic modeling of trolling-mode AFM: Considering effects of cantilever torsion, nanoneedle flexibility and liquid-nanoneedle interactions.

Trolling mode atomic force microscope (TR-Mode AFM) significantly reduces the hydrodynamic drag generated during operation in liquid environments. This is achieved by utilizing a long nanoneedle and keeping the cantilever out of liquid. In this research, a continuous mathematical model is developed to study TR-Mode AFM dynamics near a sample submerged in the liquid. Effects of cantilever torsion, nanoneedle flexibility, and liquid-nanoneedle interactions are considered in the model. In order to derive the equations of motion, Hamilton's principle and assumed mode method are used. System operation in dynamic mode is numerically simulated and the accuracy of the results is verified by comparison with the results of the finite element method. Displacements of different components of the system are also compared with each other and the dominant displacements are determined.

Multivariable control of the bifurcation and harmonic perturbations to improve the performance of air-handling units.

In this research, nonlinear dynamics of an air-ehandling unit (AHU) is studied for tracking objectives, in the presence of harmonic perturbations. Three arbitrary realistic set-paths are considered for the indoor temperature and relative humidity. Two controllers based on feedback linearization (FBL) and pole placement approaches are designed to preserve the dynamic system around the desired tracking paths. It is shown that FBL controller works efficiently in bifurcation control and transforms the quasi-periodic limit cycles into the periodic ones (and consequently comfortable indoor conditions). In addition, FBL controller guarantees suppression of larger periodic limit cycles into the smaller ones, while it requires the lower air and cold water flow rates with less oscillatory behavior (in comparison with the pole-placement controller). However, it is observed that FBL controller fails in bifurcation control when the disturbance frequency increases. Re-tuning the dynamic gains of FBL controller is essential under such conditions.

Multivariable robust adaptive sliding mode control of an industrial boiler-turbine in the presence of modeling imprecisions and external disturbances: A comparison with type-I servo controller.

To guarantee the safety and efficient performance of the power plant, a robust controller for the boiler-turbine unit is needed. In this paper, a robust adaptive sliding mode controller (RASMC) is proposed to control a nonlinear multi-input multi-output (MIMO) model of industrial boiler-turbine unit, in the presence of unknown bounded uncertainties and external disturbances. To overcome the coupled nonlinearities and investigate the zero dynamics, input-output linearization is performed, and then the new decoupled inputs are derived. To tackle the uncertainties and external disturbances, appropriate adaption laws are introduced. For constructing the RASMC, suitable sliding surface is considered. To guarantee the sliding motion occurrence, appropriate control laws are constructed. Then the robustness and stability of the proposed RASMC is proved via Lyapunov stability theory. To compare the performance of the purposed RASMC with traditional control schemes, a type-I servo controller is designed. To evaluate the performance of the proposed control schemes, simulation studies on nonlinear MIMO dynamic system in the presence of high frequency bounded uncertainties and external disturbances are conducted and compared. Comparison of the results reveals the superiority of proposed RASMC over the traditional control schemes. RAMSC acts efficiently in disturbance rejection and keeping the system behavior in desirable tracking objectives, without the existence of unstable quasi-periodic solutions.

Adaptive robust control of cancer chemotherapy in the presence of parametric uncertainties: a comparison between three hypotheses.

In this paper, an adaptive robust control strategy is developed for the manipulation of drug usage and consequently the tumor volume in cancer chemotherapy. Three nonlinear mathematical cell-kill models including log-kill hypothesis, Norton-Simon hypothesis and E(max) hypothesis are considered in the presence of uncertainties. The Lyapunov stability theorem is used to investigate the global stability and tracking convergence of the process response. For the first time, performance of the uncertain process is investigated and compared for three nonlinear models. In addition, the effects of treatment period, initial value of tumor volume (carrying capacity) and the uncertainty amount on dynamic system behaviour are studied. Through a comprehensive evaluation, results are presented and compared for three cell-kill models. According to the results, for a wide range of model uncertainties, the adaptive controller guarantees the robust performance. However, for a given treatment period, more variation in drug usage is required as the amount of model uncertainty increases. Moreover, for both the nominal and uncertain models, less drug usage is required as the treatment period increases.

Simulation of movement in three-dimensional musculoskeletal human lumbar spine using directional encoding-based neurocontrollers.

Despite development of accurate musculoskeletal models for human lumbar spine, the methods for prediction of muscle activity patterns in movements lack proper association with corresponding sensorimotor integrations. This paper uses the directional information of the Jacobian of the musculoskeletal system to orchestrate adaptive critic-based fuzzy neural controller modules for controlling a complex nonlinear redundant musculoskeletal system. The proposed controller is used to control a 3D 3-degree of freedom (DOF) musculoskeletal model of trunk, actuated by 18 muscles. The controller is capable of learning to control from sensory information, without relying on pre-assumed model parameters. Simulation results show satisfactory tracking of movements and the simulated muscle activation patterns conform to previous EMG experiments and optimization studies. The proposed controller can be used as a computationally inexpensive muscle activity generator to distinguish between neural and mechanical contributions to movement and for study of sensory versus motor origins of motor function and dysfunction in human spine.

Optimal robust control of drug delivery in cancer chemotherapy: a comparison between three control approaches.

During the drug delivery process in chemotherapy, both of the cancer cells and normal healthy cells may be killed. In this paper, three mathematical cell-kill models including log-kill hypothesis, Norton-Simon hypothesis and Emax hypothesis are considered. Three control approaches including optimal linear regulation, nonlinear optimal control based on variation of extremals and H∞-robust control based on µ-synthesis are developed. An appropriate cost function is defined such that the amount of required drug is minimized while the tumor volume is reduced. For the first time, performance of the system is investigated and compared for three control strategies; applied on three nonlinear models of the process. In additions, their efficiency is compared in the presence of model parametric uncertainties. It is observed that in the presence of model uncertainties, controller designed based on variation of extremals is more efficient than the linear regulation controller. However, H∞-robust control is more efficient in improving robust performance of the uncertain models with faster tumor reduction and minimum drug usage.

A non-linear mass-spring model for more realistic and efficient simulation of soft tissues surgery.

An extension to the classical mass-spring model for more realistic simulation of soft tissues for surgery simulation was proposed. The conventional equations of mass-spring model were generalized for non-linear springs, and model parameters were tuned using experimental data. Results show that the proposed model is fast and interactive, and also demonstrate the typical nonlinear and visco-elastic behaviors of soft tissues well.