Synergies and end-effector kinematics in upper limb movements

When humans perform movements repeatedly, they are never completely the same. This is possible because many degrees of freedom (DOF) of the human motor system are involved when performing a motor action. In most cases, the number of DOF involved exceeds the minimum necessary to complete the motor task at hand. This results in many possible solutions for a given task, which is the so-called redundancy problem. To coordinate these redundant degrees of freedom (DOF), synergies are often proposed. A synergy is defined as the temporary linking of DOF into task-specific units. Kay (1988) described the emergence of a synergy as the first step of a two-step constraining process due to the interactions amongst environment, organism, and task constraints. In the second step, the constraints act on the synergy, resulting in the specific behavior. This two-step process was examined by looking at the influence of task constraints on synergies, on end-effector kinematics, and on both levels concurrently. The first step of the two-step process was assessed using the uncontrolled manifold analysis of joint angle variability and the second step was assessed using end-effector kinematics. The results revealed that task constraints influenced synergies and end-effector kinematics independently. More importantly, the results of both synergy and end-effector level demonstrated that some constraints are mainly involved in the first step of the process, whereas other constraints mainly influence the second step of the process. This suggests that a two-step process is at play to coordinate the redundant DOF

Next move in movement disorders (NEMO):Developing a computer-aided classification tool for hyperkinetic movement disorders

Introduction: Our aim is to develop a novel approach to hyperkinetic movement disorder classification, that combines clinical information, electromyography, accelerometry and video in a computer-aided classification tool. We see this as the next step towards rapid and accurate phenotype classification, the cornerstone of both the diagnostic and treatment process. Methods and analysis: The Next Move in Movement Disorders (NEMO) study is a cross-sectional study at Expertise Centre Movement Disorders Groningen, University Medical Centre Groningen. It comprises patients with single and mixed phenotype movement disorders. Single phenotype groups will first include dystonia, myoclonus and tremor, and then chorea, tics, ataxia and spasticity. Mixed phenotypes are myoclonus-dystonia, dystonic tremor, myoclonus ataxia and jerky/tremulous functional movement disorders. Groups will contain 20 patients, or 40 healthy participants. The gold standard for inclusion consists of interobserver agreement on the phenotype among three independent clinical experts. Electromyography, accelerometry and three-dimensional video data will be recorded during performance of a set of movement tasks, chosen by a team of specialists to elicit movement disorders. These data will serve as input for the machine learning algorithm. Labels for supervised learning are provided by the expert-based classification, allowing the algorithm to learn to predict what the output label should be when given new input data. Methods using manually engineered features based on existing clinical knowledge will be used, as well as deep learning methods which can detect relevant and possibly new features. Finally, we will employ visual analytics to visualise how the classification algorithm arrives at its decision. Ethics and dissemination: Ethical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases

Synergies et cinématiques de l'effecteur final dans les mouvements des membres supérieurs

Lorsque des êtres humains exécutent des mouvements de manière répétée, ceux-ci ne sont jamais complètement les mêmes. Cela s'explique par le fait que de nombreux degrés de liberté (DDL) du système moteur humain sont impliqués dans l'exécution d'un acte moteur. Dans la plupart des cas, le nombre de DDL mis en jeu excède le minimum nécessaire pour exécuter la tâche motrice à accomplir. Pour coordonner ces DDL redondants, des synergies sont souvent proposées. Une synergie est définie comme la liaison temporaire de DDL au sein d'unités spécifiques à une tâche. Kay a décrit l'émergence d'une synergie comme étant la première étape d'un processus contraignant en deux étapes dû aux interactions entre l'environnement, l'organisme et les contraintes de la tâche. Au cours de la seconde étape, les contraintes agissent sur la synergie, entraînant le comportement spécifique. Ce processus en deux étapes a été étudié en considérant l'influence des contraintes de la tâche sur les deux niveaux. La première étape du processus en deux étapes a été évaluée au moyen de l'analyse Uncontrolled Manifold de la variabilité des angles articulaires et la seconde étape à l'aide de la cinématique de l'effecteur final. Les résultats du niveau simultané des synergies et de l'effecteur final ont démontré que certaines contraintes sont principalement impliquées dans la première étape du processus, alors que d'autres influencent principalement la seconde étape du processus. En d'autres termes, des contraintes de tâche différentes sont impliquées dans chaque étape du processus contraignant en deux étapes, ce qui semble suggérer qu'un processus en deux étapes est à l'œuvre pour coordonner les DDL redondants.When humans perform movements repeatedly, they are never completely the same. This is possible because many degrees of freedom (DOF) of the human motor system are involved when performing a motor action. In most cases, the number of DOF involved exceeds the minimum necessary to complete the motor task at hand. To coordinate these DOF, synergies are often proposed. A synergy is defined as the temporary linking of DOF into task-specific units. Kay (1988) described the emergence of a synergy as the first step of a two-step constraining process due to the interactions amongst environment, organism, and task constraints. In the second step, the constraints act on the synergy, resulting in the specific behavior. This two-step process was examined by looking at the influence of task constraints on synergies, on end-effector kinematics, and on both levels concurrently. To analyze the first step of the two-step process, the emergence of a synergy, was assessed using the uncontrolled manifold analysis of joint angle variability and the second step, the emergence of the specific behavior, was assessed using end-effector kinematics. The results revealed that task constraints influenced synergies and end-effector kinematics independently. More importantly, the results of both synergy and end-effector level demonstrated that some constraints are mainly involved in the first step of the process, whereas other constraints mainly influence the second step of the process. That is, different task constraints are involved in each step of the two-step constraining process, suggesting that a two-step process is at play to coordinate the redundant DOF

Task constraints act at the level of synergies and at the level of end-effector kinematics in manual reaching and manual lateral interception.

International audienceTo coordinate the redundant degrees of freedom (DOF) in the action system, synergies are often proposed. Synergies organi e DOF in temporary task-specific units emerging from interactions amongst task, organism, and environmental constraints. We examined whether task constraints affect synergies, end-effector kinematics, or both. To this end, we compared synergies and end-effector kinematics when participants (N = 15) performed discrete movements of identical amplitude in manual reaching (stationary targets) and manual lateral interception (moving targets, with different angles of approach). We found that time-velocity profiles were roughly symmetric in reaching, whereas they had a longer decelerative tail and showed an angle-ofapproach effect in interception. Uncontrolled Manifold analyses showed that in all conditions joint angle variability was primarily co-variation, indicating a synergistic organi ation. The analysis on the clusters of joint angle configurations demonstrated differences between reaching and interception synergies, whereas more similar synergies were used within interception conditions. This implies that some task constraints operate at the level of synergies while other task constraints only affect end-effector kinematics. The results support a two-step process in the organi ation of DOF, consisting of synergy formation and further constraining of synergies to produce the actual movement, as proposed by Kay (1988)

The development of consistency and flexibility in manual pointing during middle childhood

Goal-directed actions become truly functional and skilled when they are consistent yet flexible. In manual pointing, end-effector consistency is characterized by the end position of the index fingertip, whereas flexibility in movement execution is captured by the use of abundant arm-joint configurations not affecting the index finger end position. Because adults have been shown to exploit their system's flexibility in challenging conditions, we wondered whether during middle childhood children are already able to exploit motor flexibility when demanded by the situation. We had children aged 5-10 years and adults perform pointing movements in a nonchallenging and challenging condition. Results showed that end-effector errors and flexibility in movement execution decreased with age. Importantly, only the 9-10-year-olds and adults showed increased flexibility in the challenging condition. Thus, while consistency increases and flexibility decreases during mid-childhood development, from the age of nine children appear able to employ more flexibility with increasing task demands

Comparing Different Methods to Create a Linear Model for Uncontrolled Manifold Analysis

An essential step in uncontrolled manifold analysis is creating a linear model that relates changes in elemental variables to changes in performance variables. Such linear models are usually created by means of an analytical method. However, a multiple regression analysis is also suggested. Whereas the analytical method includes only averages of joint angles, the regression method uses the distribution of all joint angles. We examined whether the latter model is more suitable to describe manual reaching movements. The relation between estimated and measured fingertip-position deviations from the mean of individual trials, the relation between fingertip variability and nongoal-equivalent variability, goal-equivalent variability, and nongoal-equivalent variability indicated that the linear model created with the regression method gives a more accurate description of the reaching data. Therefore, we suggest the usage of the regression method to create the linear model for uncontrolled manifold analysis in tasks that require the approximation of the linear model

Summary of ANOVA results on variance of joint angles (JA), GEV_log, NGEV_log, and RATIO_log with factors Group (control and experimental), Instant (at the obstacle and at the end of movement), and Condition (pretest and posttest).

<p>Summary of ANOVA results on variance of joint angles (JA), GEV_log, NGEV_log, and RATIO_log with factors Group (control and experimental), Instant (at the obstacle and at the end of movement), and Condition (pretest and posttest).</p

End-effector kinematics per condition and group.

<p>End-effector kinematics per condition and group.</p

Joint angle variance, GEV, NGEV and flexibility for each Instant in the pretest and during practice per obstacle.

<p>Panel A depicts joint angle variance in blue, GEV in green and NGEV in red and panel B depicts RATIO. In each panel the dotted lines correspond with Instant at the obstacle while the solid lines correspond with Instant at the end of the movement. The pretest is presented on the left and practice on the right. The error bars display the standard error of the mean.</p

Machine learning basic concepts for the movement disorders specialist

In this contribution, we provide a basic introduction to key concepts of Machine Learning (ML). ML can be considered the subfield of Artificial intelligence (AI) which concerns the extraction of information from example data in order to enable computer systems to learn specific tasks and improve their performance. We first introduce most important ML paradigms: unsupervised and supervised learning, which can be associated with typical tasks such as clustering and dimensionality reduction or classification and regression, respectively. Key concepts are illustrated in terms of two imaginary “toy” data sets: the first one represents the properties of a set of various vegetables and fruit. The second one relates to data that could be acquired from a cohort of subjects in the context of movement disorders. For the sake of clarity, emphasis is put on concepts of supervised learning, in particular on classification problems. Examples of important ML methods are illustrated in terms of the above-mentioned imaginary data sets. Without going into technical details, we introduce popular approaches such as linear classifiers, neural networks and prototype-based systems. Moreover, the typical practical workflow of ML, including training, validation, and working phase is outlined. We conclude by briefly discussing issues of practical relevance such as the interpretability of ML models and Explainable AI (XAI). Being far from providing a complete overview of the very broad area of Machine Learning, this chapter should provide a starting point for the further exploration of this very active field of research and its increasing importance in medical data analysis