This paper presents a novel modular recurrent neural network based on the pipelined architecture (PRWNN) to reduce the computational complexity and improve the performance of the recurrent wavelet neural network (RWNN). The PRWNN inherits the modular architectures of the pipelined recurrent neural network proposed by Haykin and Li and is made up of a number of RWNN modules that are interconnected in a chained form. Since those modules of the PRWNN can be simultaneously performed in a pipelined parallelism fashion, this would lead to a crucial improvement of computational efficiency. Furthermore, owing to the cascade interconnection of dynamic modules, the performance of the PRWNN can be further enhanced. An adaptive gradient algorithm based on the real-time recurrent learning is derived to suit for the modular PRWNN. Simulation examples are given to evaluate the effectiveness of the PRWNN model on the identification of nonlinear dynamic systems and analysis of sunspot number time series. According to simulation results, it is clearly shown that the PRWNN provides impressive better performance in comparison with the single RWNN model.

Identification of Nonlinear Dynamic System Using a Novel Recurrent Wavelet Neural Network Based on the Pipelined Architecture

Sit-to-stand (STS) transfers are a common human task which involves complex sensorimotor processes to control the highly nonlinear musculoskeletal system. In this paper, typical unassisted and assisted human STS transfers are formulated as optimal feedback control problem that finds a compromise between task end-point accuracy, human balance, energy consumption, smoothness of motion and control and takes further human biomechanical control constraints into account. Differential dynamic programming is employed, which allows taking the full, nonlinear human dynamics into consideration. The biomechanical dynamics of the human is modeled by a six link rigid body including leg, trunk and arm segments. Accuracy of the proposed modelling approach is evaluated for different human healthy and patient/elderly subjects by comparing simulations and experimentally collected data. Acceptable model accuracy is achieved with a generic set of constant weights that prioritize the different criteria. Finally, the proposed STS model is used to determine optimal assistive strategies suitable for either a person with specific body segment weakness or a more general weakness. These strategies are implemented on a robotic mobility assistant and are intensively evaluated by 33 elderlies, mostly not able to perform unassisted STS transfers. The validation results show a promising STS transfer success rate and overall user satisfaction.

/pdf/human-sit-to-stand-transfer-modeling-towards-intuitive-and-1zcvhzrxsz.pdf

Human sit-to-stand transfer modeling towards intuitive and biologically-inspired robot assistance

Until quite recently spinal disorder problems in the U.S. have been operated by fusing cervical vertebrae instead of replacement of the cervical disc with an artificial disc. Cervical disc replacement is a recently approved procedure in the U.S. It is one of the most challenging surgical procedures in the medical field due to the deficiencies in available diagnostic tools and insufficient number of surgical practices For physicians and surgical instrument developers, it is critical to understand how to successfully deploy the new artificial disc replacement systems. Without proper understanding and practice of the deployment procedure, it is possible to injure the vertebral body. Mixed reality (MR) and virtual reality (VR) surgical simulators are becoming an indispensable part of physicians’ training, since they offer a risk free training environment. In this study, MR simulation framework and intricacies involved in the development of a MR simulator for the rasping procedure in artificial cervical disc replacement (ACDR) surgery are investigated. The major components that make up the MR surgical simulator with motion tracking system are addressed. A mixed reality surgical simulator that targets rasping procedure in the artificial cervical disc replacement surgery with a VICON motion tracking system was developed. There were several challenges in the development of MR surgical simulator. First, the assembly of different hardware components for surgical simulation development that involves knowledge and application of interdisciplinary fields such as signal processing, computer vision and graphics, along with the design and placements of sensors etc . Second challenge was the creation of a physically correct model of the rasping procedure in order to attain critical forces. This challenge was handled with finite element modeling. The third challenge was minimization of error in mapping movements of an actor in real model to a virtual model in a process called registration. This issue was overcome by a two-way (virtual object to real domain and real domain to virtual object) semi-automatic registration method. The applicability of the VICON MR setting for the ACDR surgical simulator is demonstrated. The main stream problems encountered in MR surgical simulator development are addressed. First, an effective environment for MR surgical development is constructed. Second, the strain and the stress intensities and critical forces are simulated under the various rasp instrument loadings with impacts that are applied on intervertebral surfaces of the anterior vertebrae throughout the rasping procedure. Third, two approaches are introduced to solve the registration problem in MR setting. Results show that our system creates an effective environment for surgical simulation development and solves tedious and time-consuming registration problems caused by misalignments. Further, the MR ACDR surgery simulator was tested by 5 different physicians who found that the MR simulator is effective enough to teach the anatomical details of cervical discs and to grasp the basics of the ACDR surgery and rasping procedure

/pdf/mixed-reality-simulation-of-rasping-procedure-in-artificial-2rf8yq654b.pdf

Mixed reality simulation of rasping procedure in artificial cervical disc replacement (ACDR) surgery

Human voluntary motion exhibits complex and nonlinear movement profiles. Many physiologists have studied human sit-to-stand (STS) task for healthy subjects and patients, and collected data for joints angular profiles. In this study, we synthesize some the data to a linear reference model. This reference model thus provides a basis to generate an ideal angular profile for sit to stand task. We use these reference angular profiles to simulate the STS movement with feedback controller. Our simulation shows that these reference angular profiles provide physiological relevant model to use with feedback controller design for analysis of STS movement.

Synthesis ofAngular Profi'les forBipedal Sit-to-Stand Movement

Biomechanical movements have been subject of interest for several centuries to different researchers for various aspects. Physiologically human biomechanical sit to stand movement can be defined in different phases and regions. A four link biomechanical model of human stable movement is used to develop the local linear models for each phase from sitting on chair to an upright standing position. These local models are then integrated into a fuzzy model with Gaussian membership function. The knee flexion angle during sit to stand movement provides the criterion for determining the weights of fuzzy membership functions. H 2 dynamic optimal controller is designed for each local linear model and integrated with the fuzzy model. The joint's torque or inputs are computed by using linear fuzzy model based H2 controller for linear and nonlinear biomechanical models. A reference trajectory is introduced to track the knee flexion angle error for smooth and physiological relevant sit to stand transfer. Simulation results of angular profiles and kinematics variables demonstrate the applicability of the fuzzy modeling with H2 full order and reduced order controller for the biomechanical sit to stand movement

A Fuzzy Biomechanical Model with H2 Control System for Sit-to-Stand Movement

A four link biomechanical model of human stable movement is studied through fuzzy modeling and optimal control. Physiologists describe human sit-to-stand movement in different phases; a local linear model is developed for each phase and integrating all local models with Gaussian membership functions. These local linear models of sit-to-stand movement are generated from the general nonlinear model. The knee flexion during sit to stand movement provides the criterion for determining the weights of fuzzy membership functions. The torque inputs for the fuzzy model are computed by using optimal control techniques. In this paper we study a linear fuzzy model based H2 controller for linear and nonlinear plant. Linear fuzzy Kalman filter is designed for estimating the states. The measurement noise in knee flexion and input noise in the plant model are analyzed through (linear quadratic Gaussian) LQG methods to determine the control inputs for the biomechanical model

A fuzzy biomechanical model for optimal control of sit-to-stand movement

We present the development of a 3D bipedal robotic model with thirteen generalized coordinates, and decoupled optimal controller design for the control of biomechanical sit-to-stand (STS) transfer. The non-linear model developed in Maple DynaFlexPro environment has three frontal and seven sagittal degrees of freedom (DOF). Three holonomic constraints ensure stationary foot placement during performance of the STS task. The controller design proceeds by decoupling the constrained and unconstrained DOF. We propose H2 and Hinfin optimal control designs for feedback control of joint torques in the constrained and unconstrained planes, respectively. We provide analytical and computer simulation results to show the applicability and performance of the decoupling controller for the control of STS task.

Bipedal modeling and decoupled optimal control design of biomechanical sit-to-stand transfer

Human voluntary movements are complex physical phenomenon and there are several physiological factors that control the movement and transient response, steady state position, speed of motion and other characteristics. Many experimentalists described variety of variables important for human balance and movement such as center of mass, center of pressure, ground reaction forces etc. In this study, we discuss a bipedal model for biomechanical sit to stand movement with optimal controller design. The cost optimization for gain scheduling is based upon physiological variables of center of mass, head position, and ground reaction forces. Our simulation results shows that movement profiles improve with this techniques and it provides better gain scheduling for different joint angles.

Physiological Cost Optimization for Bipedal Modeling with Optimal Controller Design

The neuro-physiological mechanisms involved in postural stabilization are not well understood. Human body mechanically resembles an inverted pendulum that is inherently unstable. Active and passive mechanisms at muscle and spinal level as well as visual and vestibular processes are attributed to postural stabilization. At the same time, intrinsic delays in the reflex pathways and the low-pass characteristics of the muscle response tend to limit the effectiveness of active mechanisms of stabilization. The motivation for this research was to study the relative contribution from active control (feedforward mechanisms) and passive stiffness (feedback mechanisms) in maintenance of posture and coordination of voluntary movement. We develop a multi-segment sagittal model with three degrees of freedom that included the rotation at ankle, knee, and hip joints. We propose an optimal LQR controller as the central nervous system analog in posture and movement coordination. We present analytical and simulation results to support an active-passive model of postural stabilization. Besides expanding our understanding of the stabilization processes in the body, the insight gained from this study is expected to promote awareness of the existence of optimal trajectories in the coordination of skilled voluntary movements.

Active control vs. passive stiffness in posture and movement coordination

Researchers have analyzed human movement coordination in a variety of ways using different optimization variables and functions. In this study we used a 4-link sagittal plane biomechanical model of sit-to-stand transfer to study physiological cost optimization. We regulated the movement with optimal controller design emulating central nervous system decision making. In such scheme the state and inputs were weighted by penalty functions derived from physiological variables, i.e., center of mass and ground reaction forces. We tracked the position variables through reference trajectories, which were also used to generate active components of joint torques. Net torque comprising feedback and feedforward components resulted in stable STS transfer. Our results shows that analytical model with physiological cost functions provides efficient framework for the study of STS movement.

Physiological Cost Optimization for Sit-to-Stand Transfer

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