A robotic model to investigate human motor control

Biological Cybernetics, 1999

The mechanical impedance of neuromusculoskeletal models of the human arm is studied in this paper. The model analysis provides a better understanding of the contributions of possible intrinsic and re¯exive components of arm impedance, makes clear the limitations of second-order mass-viscosity-stiness models and reveals possible task eects on the impedance. The musculoskeletal model describes planar movements of the upper arm and forearm, which are moved by six lumped muscles with nonlinear dynamics. The motor control system is represented by a neural network which combines feedforward and feedback control. It is optimized for the control of movements or for posture control in the presence of external forces. The achieved impedance characteristics depend on the conditions during the learning process. In particular, the impedance is adapted in a suitable way to the frequency content and direction of external forces acting on the hand during an isometric task. The impedance characteristics of a model, which is optimized for movement control, are similar to experimental data in the literature. The achieved stiness is, to a large extent, re¯exively determined whereas the approximated viscosity is primarily due to intrinsic attributes. It is argued that usually applied Hill-type muscle models do not properly represent intrinsic muscle stiness.

Trends in Neurosciences, 1982

… of applied biomechanics, 2004

This paper provides an overview of forward dynamic neuromusculoskeletal modeling. The aim of such models is to estimate or predict muscle forces, joint moments, and/or joint kinematics from neural signals. This is a four-step process. In the first step, muscle activation dynamics govern the transformation from the neural signal to a measure of muscle activation-a time varying parameter between 0 and 1. In the second step, muscle contraction dynamics characterize how muscle activations are transformed into muscle forces. The third step requires a model of the musculoskeletal geometry to transform muscle forces to joint moments. Finally, the equations of motion allow joint moments to be transformed into joint movements. Each step involves complex nonlinear relationships. The focus of this paper is on the details involved in the first two steps, since these are the most challenging to the biomechanician. The global process is then explained through applications to the study of predicting isometric elbow moments and dynamic knee kinetics.

2009

The project focuses on the modelling and control of a two-link planar mechanical manipulator that emulates a human arm. The arm is subjected to a vibratory excitation at a specific location on the arm while performing a trajectory tracking tasks in two dimensional space, taking into account the presence of 'muscle' elements that are mathematically modelled. A closed-loop control system is applied using an active force control (AFC) strategy to accommodate the disturbances based on a predefined set of loading and operating conditions to observe the system responses. Results of the study imply the effectiveness of the proposed method in compensating the vibration effect to produce robust and accurate tracking performance of the system. The results may serve as a useful tool in aiding the design and development of a tooling device for use in a mechatronic robot arm or even human arm (smart glove) where precise and/or robust performance is a critical factor and of considerable importance. The project is in fact complementing the ongoing research in the Faculty of Mechanical Engineering (FME), UTM that is geared towards developing a robust force control system. In addition to that, the research shall also serve as a basis for potential investigation into the field of biomedical related to the application of a robust control technique to effectively control human arm movement particularly when it is subjected to undesirable forcing. The fact that a human arm (to a certain extent) resembles a two-link mechanical linkage serves to provide an analogy leading to the following main and important hypothesis: the control of the human arm's movement can be effectively carried out using a number of control methods that make use of sensory information just like the mechanical arm counterpart. The results of the study clearly indicate that the modelled arm with 'muscle' elements can be simulated to demonstrate the effectiveness and robustness of the control techniques to suppress or reject various disturbances including vibration applied to the system taking into account a number of predefined input trajectories.

Biological Cybernetics, 2004

This paper describes a simple computational model of joint torque and impedance in human arm movements that can be used to simulate three-dimensional movements of the (redundant) arm or leg and to design the control of robots and human-machine interfaces. This model, based on recent physiological findings, assumes that (1) the central nervous system learns the force and impedance to perform a task successfully in a given stable or unstable dynamic environment and (2) stiffness is linearly related to the magnitude of the joint torque and increased to compensate for environment instability. Comparison with existing data shows that this simple model is able to predict impedance geometry well.

IEEE Transactions on Biomedical Engineering, 1997

The naturally coexisting intrinsic mechanical and reflex properties of the human elbow joint were identified simultaneously using nonlinear, time-delay, continuous-time, and dynamic models. Angular random perturbations of small amplitude and low bandwidth were applied to the joint using a computer-controlled servomotor, while the subject maintained various levels of mean background muscle torque. Joint neuromuscular dynamics were identified from the measured elbow angle and torque. Stretch reflexes were modeled nonlinearly with both dynamic and static reflex gains. A continuous-time system identification method was developed to estimate parameters of the nonlinear models directly from sampled data while retaining realistic physical or physiological interpretations. Results from six subjects showed that dynamic stretch reflex gains, joint stiffness, and viscosity generally increased with mean background muscle torque; and that dynamic stretch reflex gain was higher during muscle stretch than that during muscle shortening. More importantly, the study provided realistic simultaneous estimates of the relative contributions of intrinsic mechanical and reflex actions to net joint torque. In particular, reflexively-mediated stiffness generated a significant portion of the total joint stiffness and the percentage varied systematically with background muscle torque.

Applied Bionics and Biomechanics, 2009

One of the approaches to study the human motor system, and specifically the motor strategies implied during postural tasks of the upper limbs, is to manipulate the mechanical conditions of each joint of the upper limbs independently. At the same time, it is essential to pick up biomechanical signals and bio-potentials generated while the human motor system adapts to the new condition. The aim of this paper is twofold: first, to describe the design, development and validation of an experimental platform designed to modify or perturb the mechanics of human movement, and simultaneously acquire, process, display and quantify bioelectric and biomechanical signals; second, to characterise the dynamics of the elbow joint during postural control. A main goal of the study was to determine the feasibility of estimating human elbow joint dynamics using EMG-data during maintained posture. In particular, the experimental robotic platform provides data to correlate electromyographic (EMG) activity, kinetics and kinematics information from the upper limb motion. The platform aims consists of an upper limb powered exoskeleton, an EMG acquisition module, a control unit and a software system. Important concerns of the platform such as dependability and safety were addressed in the development. The platform was evaluated with 4 subjects to identify, using system identification methods, the human joint dynamics, i.e. visco-elasticity. Results obtained in simulations and experimental phase are introduced.

Proceedings of the 26th International Congress of Mechanical Engineering, 2021

Mechanical prosthesis designs appear throughout our history demonstrating the human desire to recover what has been lost from its physiology. With the constant evolution of technology over time, more and more complex prostheses have been developed, assimilating a wider range of functionalities for the user. Besides that, modern prostheses are still expensive and inaccessible to the low-income population.Even state-of-the-art devices are limited in terms of fidelity to the complexity of human biomechanics, some of that because their degrees of freedom are often drastically reduced. In recent years, promising technologies have emerged, with potential to offer solutions to the problems previously mentioned. Innovative ways to measure the upper limbs range of motion, artificial muscles and compact mechanisms capable of incorporating more faithful movements are some examples of technologies with potential to quickly change the scenario of prostheses and bioinspired robots. Alongside those technologies, computational models, able to represent the complex behaviour of the human arm and hand movements, are crucial for the development of control techniques that can benefit from such parameterized models. The present work offers a 34 degrees of freedom kinematic model of the upper limb, based on the Denavit-Hartenberg parameters, able to perform natural and biomechanically expected movements.

Journal of Biomechanics, 2008

The dynamic behavior of a neuromusculoskeletal system results from the complex mechanical interaction between muscle viscoelasticity resulting from (co-)contraction and afferent feedback from muscle spindles and Golgi tendon organs. As a result of the multiple interactions the individual effect of each of the structures to the overall dynamics is hard to recognize, if not impossible. Here a neuromuscular control (NMC) model is developed to analyze the functional contribution of the various physiological structures on the mechanical behavior of a limb. The dynamics of a joint are presented in admittances, i.e. the dynamic relation between input force (or torque) and the output displacement, which can be represented by either frequency or impulse response functions. With the model it can be shown that afferent feedback reduces, while muscle visco-elasticity increases, the stability margins. This implicates that there is a delicate balance between muscle co-contraction and afferent feedback, which depends on the joint specific physiological properties. The main application of the model is educational; it is implemented in a graphical user interface allowing users to explore the role of the various physiological structures on joint dynamics. Other applications of the model are more experimental, e.g. to elucidate experimentally measured admittances and to compare the quantified parameter values with the theoretically optimal ones. It is concluded that the NMC model is a useful and intuitive tool to investigate human motor control, in a theoretical as well as an experimental way. r

… 2008. BioRob 2008 …, 2008

This paper presents the characterization of the position and stiffness open loop controller for the NEURARM bio-inspired joint. A novel antagonistic non-linear actuation scheme is proposed for the NEURARM platform, a 2 DoF planar robotic arm that has been developed to imitate the principal functional features of the human arm for planar movements. The NEURARM joint has the actuation scheme based on a contractile element (a hydraulic piston) in series with a non-linear elastic element, able to mimic the force-elongation characteristic of the muscle-tendon complex. The non-linear spring is obtained by a linear tension spring rendered nonlinear by means of a specifically designed mechanism. Such actuation scheme allows the implementation of control strategies based on equilibrium point and impedance control hypotheses of human motor behavior. The preliminary results of the characterization of the open loop joint stiffness and position controller are presented.

9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005., 2005

The experimental investigation of the visco-elastic properties of the human arm has important implications in neuroscience, robotics and neuro-rehabilitation of the upper limb. In neuroscience it allows studying the mechanisms used by the central nervous system in implementing low-level strategies for motion control; in robotics it can provide useful information for the formulation of control strategies for humanrobot interaction, by taking inspiration from the biological behavior; in neuro-rehabilitation it may be a useful tool for quantitatively assessing the recovery of motor functions during motor therapy. Few mechatronic instruments exist allowing the direct measurement of visco-elastic properties of the human arm. None of them are intended to be portable and stand-alone devices, and also to be properly interfaced with existing robots as end effectors. This paper is concerned with the design of a novel mechatronic handle intended to be a portable device for the measurement of human arm visco-elastic properties. The design specifications take into account also the possibility of adapting the device to existing robots for rehabilitation motor therapy. In particular, the investigation of existing literature, both in robotics and in neuroscience, together with numerical simulations, led to the selection of the most useful technical specifications for such devices. A preliminary design of the handle is also presented and discussed.

Journal of Biomechanics, 2000

The purpose of this study was to compare passive to active testing on the kinematics of the elbow and forearm using a load-controlled testing apparatus that simulates muscle loading. Ten fresh-frozen upper extremities were tested. Active control was achieved by employing computer-controlled pneumatic actuators attached to the tendons of the brachialis, biceps, triceps, brachioradialis and pronator teres. Motion of the radius and ulna relative to the humerus was measured with an electromagnetic tracking system. Active elbow #exion produced more repeatable motion of the radius and ulna than when tested passively (p(0.05). The decrease in variability, as determined from the standard deviation of "ve successive trials in each specimen, was 76.5 and 58.0% for the varus}valgus and internal}external motions respectively (of the ulna relative to the humerus). The variability in #exion during simulated active forearm supination was 30.6% less than during passive testing. Thus under passive control, in the absence of stability provided by muscular loading across the joint, these uncontrolled motions produce increased variability amongst trials. The smooth and repeatable motions resulting from active control, that probably model more closely the physiologic state, appear to be bene"cial in the evaluation of unconstrained kinematics of the intact elbow and forearm.

Studies in systems, decision and control, 2017

— The design and implementation of a complete virtual model of a robotic system, by simulating components and control programs, can significantly impact the general efficiency of a project. Depending on the level of detail and accuracy of the simulation, there are various areas which can be investigated, all of which affect the development life cycle to a certain extent. This study describes a neuro-driven Human-Machine Interface based on the use of muscle synergies. The proposed strategy was evaluated on a NAO robot arm, by performing an online simulation with real-time constraints, within the Gazebo simulation environment. The obtained results show that it is possible to actively control an external device at all times, by using muscle synergies, without any subject-specific musculoskeletal model. Such a tecnology aims to effectively contribute on designing and developing new generation human-robot interfaces, and motion control algorithms for intelligent robotic devices.