Motor variability is inevitable in human body movements and has been addressed from various perspectives in motor neuroscience and biomechanics: it may originate from variability in neural activities, or it may reflect a large number of degrees of freedom inherent in our body movements. How to evaluate motor variability is thus a fundamental question. Previous methods have quantified (at least) two striking features of motor variability: smaller variability in the task-relevant dimension than in the task-irrelevant dimension and a low-dimensional structure often referred to as synergy or principal components. However, the previous methods cannot be used to quantify these features simultaneously and are applicable only under certain limited conditions (e.g., one method does not consider how the motion changes over time, and another does not consider how each motion is relevant to performance). Here, we propose a flexible and straightforward machine learning technique for quantifying task-relevant variability, task-irrelevant variability, and the relevance of each principal component to task performance while considering how the motion changes over time and its relevance to task performance in a data-driven manner. Our method reveals the following novel property: in motor adaptation, the modulation of these different aspects of motor variability differs depending on the perturbation schedule.
Goal-directed whole-body movements are fundamental in our daily life, sports, music, art, and other activities. Goal-directed movements have been intensively investigated by focusing on simplified movements (e.g., arm-reaching movements or eye movements); however, the nature of goal-directed whole-body movements has not been sufficiently investigated because of the high-dimensional nonlinear dynamics and redundancy inherent in whole-body motion. One open question is how to overcome high-dimensional nonlinear dynamics and redundancy to achieve the desired performance. It is possible to approach the question by quantifying how the motions of each body part at each time point contribute to movement performance. Nevertheless, it is difficult to identify an explicit relation between each motion element (the motion of each body part at each time point) and performance as a result of the high-dimensional nonlinear dynamics and redundancy inherent in whole-body motion. The current study proposes a data-driven approach to quantify the relevance of each motion element to the performance. The current findings indicate that linear regression may be used to quantify this relevance without considering the high-dimensional nonlinear dynamics of whole-body motion.
In this paper, we propose a data-driven technique to detect task-relevant and task-irrelevant motion components with categorical task outcomes. Our method relies on a linear regression technique for solving classification problems, such as logistic regression 7. For example, logistic regression enables classification of whether the current motion data are associated with throwing a fastball or breaking ball. Our data-driven method can be applied even when the relation between motion data and outcome is unknown: the relation can still be estimated in a data-driven manner. Along with recent data-driven approaches in biomechanics that focused on unsupervised methods 8,9 , we rely on supervised methods to address the task-relevant and task-irrelevant components with categorical outcomes in a data-driven manner. Notably, our current mathematical framework is the same as that used in our previous methods 5,6 , which focused on continuous task outcomes by applying a linear regression technique. Along with our previous methods, we propose a unified data-driven approach to detect task-relevant and task-irrelevant motion components for multiple kinds of task outcomes. Methods participants. Eight healthy volunteers (aged 18-21 years, four females) participated in our experiment for two days (not consecutively). All the participants were informed of the experimental procedures and their conformance with the Declaration of Helsinki, and all participants provided written informed consent before the start of the experiments. All the procedures were approved by the Ethics Committee of the Tokyo University of Agriculture and Technology.
How does the central nervous system (CNS) control our bodies, including hundreds of degrees of freedom (DoFs)? A hypothesis to reduce the number of DoFs posits that the CNS controls groups of joints or muscles (i.e., modules) rather than each joint or muscle independently. Another hypothesis posits that the CNS primarily controls motion components relevant to task achievements (i.e., task-relevant components). Although the two hypotheses are examined intensively, the relationship between the two concepts remains unknown, e.g., unimportant modules may possess task-relevant information. Here, we propose a framework of task-relevant modules, i.e., modules relevant to task achievements, while combining the two concepts mentioned above in a data-driven manner. To examine the possible role of the task-relevant modules, we examined the modulation of the task-relevant modules in a motor adaptation paradigm in which trial-to-trial modifications of motor output are observable. The task-relevant modules, rather than conventional modules, showed adaptation-dependent modulations, indicating the relevance of task-relevant modules to trial-to-trial updates of motor output. Our method provides insight into motor control and adaptation via an integrated framework of modules and task-relevant components.
1Motor variability is inevitable in our body movements and is discussed from several various perspectives 2 in motor neuroscience and biomechanics; it can originate from the variability of neural activities, it can 3 reflect a large degree of freedom inherent in our body movements, it can decrease muscle fatigue, or it 4 can facilitate motor learning. How to evaluate motor variability is thus a fundamental question in motor 5 neuroscience and biomechanics. Previous methods have quantified (at least) two striking features of motor 6 variability; the smaller variability in the task-relevant dimension than in the task-irrelevant dimension 7 and the low-dimensional structure that is often referred to as synergy or principal component. However, 8 those previous methods were not only unsuitable for quantifying those features simultaneously but also 9 applicable in some limited conditions (e.g., a method cannot consider motion sequence, and another 10 method cannot consider how each motion is relevant to performance). Here, we propose a flexible and 11 straightforward machine learning technique that can quantify task-relevant variability, task-irrelevant 12 variability, and the relevance of each principal component to task performance while considering the 13 motion sequence and the relevance of each motion sequence to task performance in a data-driven manner. 14 We validate our method by constructing a novel experimental setting to investigate goal-directed and 15 whole-body movements. Furthermore, our setting enables the induction of motor adaptation by using 16 perturbation and evaluating the modulation of task-relevant and task-irrelevant variabilities through 17 motor adaptation. Our method enables the identification of a novel property of motor variability; the 18 modulation of those variabilities differs depending on the perturbation schedule. Although a gradually 19 imposed perturbation does not increase both task-relevant and task-irrelevant variabilities, a constant 20 perturbation increases task-relevant variability. 21 In our daily life, we can repeatedly achieve desired movements, such as grasping a cup, throwing a ball, 23 and playing the piano. To achieve the desired movements, our motor system needs to resolve at least two 24 difficulties inherent in our body motion [1]. A difficulty is movement variability. Due to the variability 25 inherent in various stages such as sensing sensory information [2], neural activities in motor planning [3], 26 or muscle activities in motor execution [4], even sophisticated athletes and musicians cannot repeat the 27 same movements. Our motor systems somehow tame those variabilities to achieve the desired movements 28 [5]. Another difficulty is a large degree of freedom (DoF) inherent in our motor system [1, 6]. The number 29 of joints, muscles, and neurons are more than necessary to achieve the desired movements, resulting in 30 an infinite number of joint configurations, muscle activities, and neural activities that can correspond to 31 the desired move...
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