Dynamics of wrist and forearm rotations

Parkinsonian Tremor as Unstable Feedback in a Physiologically Consistent Control Framework

Parkinson's disease (PD) is characterized by decreased dopamine in the basal ganglia that causes excessive tonic inhibition of the thalamus. This excessive inhibition seems to explain inhibitory motor symptoms in PD, but the source of tremor remains unclear. This paper investigates how neural inhibition may change the closed-loop characteristics of the human motor control system to determine how this established pathophysiology could produce tremor. The rate-coding model of neural signals suggests increased inhibition decreases signal amplitude, which could create a mismatch between the closed-loop dynamics and the internal models that overcome proprioceptive feedback delays. This paper aims to identify a candidate model structure with decreased-amplitude-induced tremor in PD that also agrees with previously recorded movements of healthy and cerebellar patients. The optimal feedback control theory of human motor control forms the basis of the model. Key additional elements include gating of undesired movements via the basal ganglia-thalamus-motor cortex circuit and the treatment of the efferent copy of the control input as a measurement in the state estimator. Simulations confirm the model's ability to capture tremor in PD and also demonstrate how disease progression could affect tremor and other motor symptoms, providing insight into the existence of tremor and non-tremor phenotypes. Altogether, the physiological underpinnings of the model structure and the agreement of model predictions with clinical observations provides support for the hypothesis that unstable feedback produces parkinsonian tremor. Consequently, these results also support the associated framework for the neuroanatomy of human motor control.

Development of a New Control System for a Rehabilitation Robot Using Electrical Impedance Tomography and Artificial Intelligence

In this study, we present a tomography-based control system for a rehabilitation robot using a novel approach to assess advancement and a dynamic model of the system. In this model, the torque generated by the robot and the impedance of the patient's hand are used to determine each step of the rehabilitation. In the proposed control architecture, a regression model is developed and implemented based on the extraction of tomography signals to estimate the muscles state. During the rehabilitation session, the torque applied by the patient is adjusted according to this estimation. The first step of this protocol is to calculate the subject-specific parameters. These include the axis offset, inertia parameters, passive damping and stiffness. The second step involves identifying the other elements of the model, such as the torque resulting from interaction. In this case, the robot will calculate the torque generated by the patient. The developed robot-based solution and the suggested protocol were tested on different participants and showed promising results. First, the prediction of the impedance-position relationship was evaluated, and the prediction was below 2% error. Then, different participants with different impedances were tested, and the results showed that the control system controlled the force and position for each participant individually.

Movement preferences of the wrist and forearm during activities of daily living

BACKGROUND

During activities of daily living, the main degrees of freedom of the forearm and wrist-forearm pronation-supination (PS), wrist flexion-extension (FE), and wrist radial-ulnar deviation (RUD)-combine seamlessly to allow the hand to engage with and manipulate objects in our environment. Yet the combined behavior of these three degrees of freedom is relatively unknown.

PURPOSE

To provide a characterization of natural forearm and wrist kinematics (joint configuration, movement direction, and speed) during activities of daily living.

STUDY DESIGN

This is a descriptive cross-sectional study.

METHODS

Ten healthy subjects performed 24 activities of daily living chosen to represent a wide variety of activities, while we measured their PS, FE, and RUD angles using electromagnetic motion capture. The orientation of the forearm and wrist was represented in the three-dimensional "configuration space" spanned by PS, FE, and RUD. From the time course of forearm and wrist orientation in configuration space, we extracted three-dimensional distributions of joint configuration, movement direction, and speed.

RESULTS

Most joint configurations were focused in a relatively small area: subjects spent roughly 50% of the time in the central 20% of their functional range of motion. Some movement directions were significantly more common than others (p < 0.001); in particular, the direction of the dart-thrower's motion (DTM) was about three times more common than motion perpendicular to it. Most movements were slow: the likelihood of moving at increasing speeds dropped off exponentially. Interestingly, the most common high-speed motion combined the DTM with a twist from pronation to supination. As this motion allows one to pick up an object in front of one's body and bring it to the head, it is essential for self-care. Thus, although many activities of daily living follow the DTM without significant forearm rotation, the greatest importance of the DTM may lie in its combination with forearm rotation.

CONCLUSIONS

Despite the wide variety of activities, we found evidence of preferred movement behavior, and this behavior showed significant coupling between the wrist and forearm.

A New Wrist-Forearm Rehabilitation Protocol Integrating Human Biomechanics and SVM-Based Machine Learning for Muscle Fatigue Estimation

In this research, a new remote rehabilitation system was developed that integrates an IoT-based connected robot intended for wrist and forearm rehabilitation. In fact, the mathematical model of the wrist and forearm joints was developed and integrated into the main controller. The proposed new rehabilitation protocol consists of three main sessions: the first is dedicated to the extraction of the passive components of the dynamic model of wrist-forearm biomechanics while the active components are extracted in the second session. The third session consists of performing continuous exercises using the determined dynamic model of the forearm-wrist joints, taking into account the torque generated by muscle fatigue. The main objective of this protocol is to determine the state level of the affected wrist and above all to provide a dynamic model in which the torque generated by the robot and the torque supplied by the patient are combined, taking into account the constraints of fatigue. A Support Vector Machine (SVM) classifier is designed for the estimation of muscle fatigue based on the features extracted from the electromyography (EMG) signal acquired from the patient. The results show that the developed rehabilitation system allows a good progression of the joint's range of motion as well as the resistive-active torques.

Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation

In this study, we present an IoT-based robot for wrist rehabilitation with a new protocol for determining the state of injured muscles as well as providing dynamic model parameters. In this model, the torque produced by the robot and the torque provided by the patient are determined and updated taking into consideration the constraints of fatigue. Indeed, in the proposed control architecture based on the EMG signal extraction, a fuzzy classifier was designed and implemented to estimate muscle fatigue. Based on this estimation, the patient’s torque is updated during the rehabilitation session. The first step of this protocol consists of calculating the subject-related parameters. This concerns axis offset, inertial parameters, passive stiffness, and passive damping. The second step is to determine the remaining component of the wrist model, including the interaction torque. The subject must perform the desired movements providing the torque necessary to move the robot in the desired direction. In this case, the robot applies a resistive torque to calculate the torque produced by the patient. After that, the protocol considers the patient and the robot as active and all exercises are performed accordingly. The developed robotics-based solution, including the proposed protocol, was tested on three subjects and showed promising results.

Optimal Control Perspective on Parkinson's Disease: Increased Delay Between State Estimator and Controller Produces Tremor

Parkinson's disease produces tremor in a large subset of patients despite generally inhibiting movement. The pathophysiology of parkinsonian tremor is unclear, leading to uncertainty in how and why treatments reduce tremor with varying effectiveness. Models for parkinsonian tremor attempt to explain the underlying principles of tremor generation in the central nervous system, often focusing on neural activity of specific substructures. In contrast, control system approaches to modeling the human motor system provide qualitative results that help inform conclusions from clinical studies. This article uses an optimal control approach to investigate the hypothesis that an increased delay in the central nervous system-unaccounted by delay compensation mechanisms-produces parkinsonian tremor. This hypothesis is motivated by the excessive inhibition projected from the basal ganglia to the thalamus in Parkinson's disease. The thalamus relays signals from the cerebellum to the primary motor cortex: previous mapping of optimal control components indicates this prospective delay exists between the estimator (cerebellum) and controller (primary motor cortex). Simulations demonstrate realistic tremor in a neuromuscular model of the wrist. In addition, changes to effort sensitivity in the optimal controller may account for some clinical features of parkinsonian tremor, including the characteristics of re-emergent tremor and the time-varying amplitude and frequency of tremor. Contextualization of the optimal control model with physiological models and clinical observations provides insight into the potential role of the basal ganglia and cerebello-thalamo-cortical circuit and how treatments like dopaminergic medications and deep brain stimulation reduce tremor.

Human Wrist Impedance Estimation Based on Impulse Response Induced by Snap-Through Buckling of Closed-Elastica

A human joint impedance estimation method employing a wearable device using the snap-through buckling of closed-elastica has been proposed in our previous study and tested for the ankle joint. In the present study, the method is applied to the wrist impedance estimation scheme. Our method aims at the task-dependent scenarios. The wrist joint is involved in a variety of tasks, where the impedance plays a key role in physical interaction with environments. Because of the unique design and actuation concept, the proposed device does not disrupt the voluntary movements of the wrist and can be used in the estimation during the user's own motion planning. In this paper, to verify our method, the wrist impedance under the quasi-stationary condition is estimated, and the results are compared with the related works. Also, we discuss the profiles of the induced impulse responses and suggest an alternative estimation manner which does not require any force (torque) information.

A geometric framework for the estimation of joint stiffness of the human wrist

Estimating joint stiffness is of paramount importance for studying human motor control and for clinical assessment of neurological diseases. Usually stiffness estimation is performed using cumbersome instrumentations (e.g. robots), and by approximating robot joint angles and torques to the human ones. This paper proposes a methodology and an experimental setup to measure wrist joint stiffness in unstructured environments, with the twofold aim of: 1) providing a geometric framework in order to derive angular displacements and torques at the wrist Flexion/Extension (FE) and Radial/Ulnar Deviation (RUD) axes of rotation, using a subject specific kinematic model; 2) suggesting an experimental setup made of two portable sensors for motion tracking and one load cell, to allow for measurements in out-of-the-lab scenarios. We tested our method on a hardware mockup of wrist kinematics, providing a ground truth for estimated angles and torques at FE and RUD joints. The experimental validation showed average absolute errors in FE and RUD angles of 0.005 rad and 0.0167 rad respectively, and an average error of FE and RUD torques of 0.006 Nm and 0.003 Nm.

Swing Velocity Profiles of Small Limbs Can Arise from Transient Passive Torques of the Antagonist Muscle Alone

In large limbs, changing motor neuron activity typically controls within-movement velocity. For example, sequential agonist-antagonist-agonist motor neuron firing typically underlies the slowing often present at the end of human reaches. In physiological movements of large limbs, antagonistic muscle passive torque is generally negligible. In small limbs, alternatively, passive torques can determine limb rest position, generate restoring movements to it, and decrease agonist-generated movement amplitude and velocity maxima. These observations suggest that, in small limbs, passive forces might also control velocity changes within movements. We investigated this issue in stick insect middle leg femur-tibia (FT) joint. During swing, the FT joint extensor muscle actively shortens and the flexor muscle passively lengthens. As in human reaching, after its initial acceleration, FT joint velocity continuously decreases. We measured flexor passive forces during imposed stretches spanning the ranges of FT joint angles, angular velocities, and movement amplitudes present in leg swings. The viscoelastic "transient" passive force that occurs during and soon after stretch depended on all three variables and could be tens of times larger than the "steady-state" passive force commonly measured long after stretch end. We combined these data, the flexor and extensor moment arms, and an existing extensor model to simulate FT joint swing. To measure only passive (flexor) muscle-dependent effects, we used constant extensor activations in these simulations. In simulations using data from ten flexor muscles, flexor passive torque could always produce swings with, after swing initiation, continuously decreasing velocities. Antagonist muscle passive torques alone can thus control within-movement velocity.

Control of redundant pointing movements involving the wrist and forearm

The musculoskeletal system can move in more ways than are strictly necessary, allowing many tasks to be accomplished with a variety of limb configurations. Why some configurations are preferred has been a focus of motor control research, but most studies have focused on shoulder-elbow or whole arm movements. This study focuses on movements involving forearm pronation-supination (PS), wrist flexion-extension (FE), and wrist radial-ulnar deviation (RUD) and elucidates how these three degrees of freedom (DOF) combine to perform the common task of pointing, which only requires two DOF. Although pointing is more sensitive to FE and RUD than to PS and could be easily accomplished with FE and RUD alone, subjects tend to involve a small amount of PS. However, why we choose this behavior has been unknown and is the focus of this paper. With the use of a second-order model with lumped parameters, we tested a number of plausible control strategies involving minimization of work, potential energy, torque, and path length. None of these control schemes robustly predicted the observed behavior. However, an alternative control scheme, hypothesized to control the DOF that were most important to the task (FE and RUD) and ignore the less important DOF (PS), matched the observed behavior well. In particular, the behavior observed in PS appears to be a mechanical side effect caused by unopposed interaction torques. We conclude that moderately sized pointing movements involving the wrist and forearm are controlled by ignoring forearm rotation even though this strategy does not robustly minimize work, potential energy, torque, or path length. NEW & NOTEWORTHY Many activities require us to point our hands in a given direction using wrist and forearm rotations. Although there are infinitely many ways to do this, we tend to follow a stereotyped pattern. Why we choose this pattern has been unknown and is the focus of this paper. After testing a variety of hypotheses, we conclude that the pattern results from a simplifying strategy in which we focus on wrist rotations and ignore forearm rotation.

Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling

OBJECTIVE

Robotic prosthetic limbs promise to replace mechanical function of lost biological extremities and restore amputees' capacity of moving and interacting with the environment. Despite recent advances in biocompatible electrodes, surgical procedures, and mechatronics, the impact of current solutions is hampered by the lack of intuitive and robust man-machine interfaces.

APPROACH

This work presents a biomimetic interface that synthetizes the musculoskeletal function of an individual's phantom limb as controlled by neural surrogates, i.e. electromyography-derived neural activations. With respect to current approaches based on machine learning, our method employs explicit representations of the musculoskeletal system to reduce the space of feasible solutions in the translation of electromyograms into prosthesis control commands. Electromyograms are mapped onto mechanical forces that belong to a subspace contained within the broader operational space of an individual's musculoskeletal system.

MAIN RESULTS

Our results show that this constraint makes the approach applicable to real-world scenarios and robust to movement artefacts. This stems from the fact that any control command must always exist within the musculoskeletal model operational space and be therefore physiologically plausible. The approach was effective both on intact-limbed individuals and a transradial amputee displaying robust online control of multi-functional prostheses across a large repertoire of challenging tasks.

SIGNIFICANCE

The development and translation of man-machine interfaces that account for an individual's neuromusculoskeletal system creates unprecedented opportunities to understand how disrupted neuro-mechanical processes can be restored or replaced via biomimetic wearable assistive technologies.

The effect of common wrist orthoses on the stiffness of wrist rotations

Wrist orthoses (also known as splints, braces, or supports) are commonly used to support or restrict the motion of a weak or injured wrist. These orthoses generally function by stiffening the wrist joint. Therefore, choosing the proper orthosis (or improving orthoses) requires that we understand their stiffness properties. In this study, we present a method for measuring the stiffness of wrist orthoses, and we apply this method to 12 of the most common wrist orthoses. We found similarities and differences between these orthoses, indicating that different orthoses have different effects on the wrist joint and, presumably, on wrist behavior. In particular, all six orthoses with a stay on the volar side or the volar and dorsal sides added a significant amount of stiffness to the wrist joint. In contrast, only one of three orthoses with a stay on the dorsal side and none of the three orthoses without stays exhibited a significant amount of stiffness, calling into question their ability to support the wrist joint. This work lays a foundation for future studies investigating the effect of wrist orthosis stiffness on wrist behavior and how wrist orthosis stiffness can be designed to produce behavior that facilitates healing.

An Extended Passive Motion Paradigm for Human-Like Posture and Movement Planning in Redundant Manipulators

A major challenge in robotics and computational neuroscience is relative to the posture/movement problem in presence of kinematic redundancy. We recently addressed this issue using a principled approach which, in conjunction with nonlinear inverse optimization, allowed capturing postural strategies such as Donders' law. In this work, after presenting this general model specifying it as an extension of the Passive Motion Paradigm, we show how, once fitted to capture experimental postural strategies, the model is actually able to also predict movements. More specifically, the passive motion paradigm embeds two main intrinsic components: joint damping and joint stiffness. In previous work we showed that joint stiffness is responsible for static postures and, in this sense, its parameters are regressed to fit to experimental postural strategies. Here, we show how joint damping, in particular its anisotropy, directly affects task-space movements. Rather than using damping parameters to fit a posteriori task-space motions, we make the a priori hypothesis that damping is proportional to stiffness. This remarkably allows a postural-fitted model to also capture dynamic performance such as curvature and hysteresis of task-space trajectories during wrist pointing tasks, confirming and extending previous findings in literature.

Task-space separation principle: a force-field approach to motion planning for redundant manipulators

In this work, we address human-like motor planning in redundant manipulators. Specifically, we want to capture postural synergies such as Donders' law, experimentally observed in humans during kinematically redundant tasks, and infer a minimal set of parameters to implement similar postural synergies in a kinematic model. For the model itself, although the focus of this paper is to solve redundancy by implementing postural strategies derived from experimental data, we also want to ensure that such postural control strategies do not interfere with other possible forms of motion control (in the task-space), i.e. solving the posture/movement problem. The redundancy problem is framed as a constrained optimization problem, traditionally solved via the method of Lagrange multipliers. The posture/movement problem can be tackled via the separation principle which, derived from experimental evidence, posits that the brain processes static torques (i.e. posture-dependent, such as gravitational torques) separately from dynamic torques (i.e. velocity-dependent). The separation principle has traditionally been applied at a joint torque level. Our main contribution is to apply the separation principle to Lagrange multipliers, which act as task-space force fields, leading to a task-space separation principle. In this way, we can separate postural control (implementing Donders' law) from various types of tasks-space movement planners. As an example, the proposed framework is applied to the (redundant) task of pointing with the human wrist. Nonlinear inverse optimization (NIO) is used to fit the model parameters and to capture motor strategies displayed by six human subjects during pointing tasks. The novelty of our NIO approach is that (i) the fitted motor strategy, rather than raw data, is used to filter and down-sample human behaviours; (ii) our framework is used to efficiently simulate model behaviour iteratively, until it converges towards the experimental human strategies.

Proximal-distal differences in movement smoothness reflect differences in biomechanics

Smoothness is a hallmark of healthy movement. Past research indicates that smoothness may be a side product of a control strategy that minimizes error. However, this is not the only reason for smooth movements. Our musculoskeletal system itself contributes to movement smoothness: the mechanical impedance (inertia, damping, and stiffness) of our limbs and joints resists sudden change, resulting in a natural smoothing effect. How the biomechanics and neural control interact to result in an observed level of smoothness is not clear. The purpose of this study is to 1) characterize the smoothness of wrist rotations, 2) compare it with the smoothness of planar shoulder-elbow (reaching) movements, and 3) determine the cause of observed differences in smoothness. Ten healthy subjects performed wrist and reaching movements involving different targets, directions, and speeds. We found wrist movements to be significantly less smooth than reaching movements and to vary in smoothness with movement direction. To identify the causes underlying these observations, we tested a number of hypotheses involving differences in bandwidth, signal-dependent noise, speed, impedance anisotropy, and movement duration. Our simulations revealed that proximal-distal differences in smoothness reflect proximal-distal differences in biomechanics: the greater impedance of the shoulder-elbow filters neural noise more than the wrist. In contrast, differences in signal-dependent noise and speed were not sufficiently large to recreate the observed differences in smoothness. We also found that the variation in wrist movement smoothness with direction appear to be caused by, or at least correlated with, differences in movement duration, not impedance anisotropy.NEW & NOTEWORTHY This article presents the first thorough characterization of the smoothness of wrist rotations (flexion-extension and radial-ulnar deviation) and comparison with the smoothness of reaching (shoulder-elbow) movements. We found wrist rotations to be significantly less smooth than reaching movements and determined that this difference reflects proximal-distal differences in biomechanics: the greater impedance (inertia, damping, stiffness) of the shoulder-elbow filters noise in the command signal more than the impedance of the wrist.

Fundamental Principles of Tremor Propagation in the Upper Limb

Although tremor is the most common movement disorder, there exist few effective tremor-suppressing devices, in part because the characteristics of tremor throughout the upper limb are unknown. To clarify, optimally suppressing tremor requires a knowledge of the mechanical origin, propagation, and distribution of tremor throughout the upper limb. Here we present the first systematic investigation of how tremor propagates between the shoulder, elbow, forearm, and wrist. We simulated tremor propagation using a linear, time-invariant, lumped-parameter model relating joint torques and the resulting joint displacements. The model focused on the seven main degrees of freedom from the shoulder to the wrist and included coupled joint inertia, damping, and stiffness. We deliberately implemented a simple model to focus first on the most basic effects. Simulating tremorogenic joint torque as a sinusoidal input, we used the model to establish fundamental principles describing how input parameters (torque location and frequency) and joint impedance (inertia, damping, and stiffness) affect tremor propagation. We expect that the methods and principles presented here will serve as the groundwork for future refining studies to understand the origin, propagation, and distribution of tremor throughout the upper limb in order to enable the future development of optimal tremor-suppressing devices.