Decomposing motion that changes over time into task-relevant and task-irrelevant components in a data-driven manner: application to motor adaptation in whole-body movements

Yokoyama

Kaneko

et al. 2020

Sci Rep

Self Cite

How the central nervous system (CNS) controls many joints and muscles is a fundamental question in motor neuroscience and related research areas. An attractive hypothesis is the module hypothesis: the CNS controls groups of joints or muscles (i.e., spatial modules) by providing time-varying motor commands (i.e., temporal modules) to the spatial modules rather than controlling each joint or muscle separately. Another fundamental question is how the CNS generates numerous repertoires of movement patterns. one hypothesis is that the cnS modulates the spatial and/or temporal modules depending on the required tasks. It is thus essential to quantify the spatial modules, the temporal modules, and the task-dependent modulation of these modules. Although previous attempts at such quantification have been made, they considered modulation either only in spatial modules or only in temporal modules. These limitations may be attributable to the constraints inherent to conventional methods for quantifying the spatial and temporal modules. Here, we demonstrate the effectiveness of tensor decomposition in quantifying the spatial modules, the temporal modules, and the taskdependent modulation of these modules without such limitations. We further demonstrate that tensor decomposition offers a new perspective on the task-dependent modulation of spatiotemporal modules: in switching from walking to running, the CNS modulates the peak timing in the temporal modules while recruiting more proximal muscles in the corresponding spatial modules.How the central nervous system (CNS) controls the human body is a fundamental question. The human body possesses 360 joints and more than 650 muscles; the CNS controls the body while somehow resolving a significant number of degrees of freedom (DoFs), in fact, more than are necessary to achieve the desired motions 1 . For example, during walking and running in daily life, these large numbers of joints and muscles should be controlled in an orchestrated manner. The module hypothesis is an influential proposal regarding how such a tremendous number of DoFs can be managed 2-6 . According to this hypothesis, the CNS effectively reduces the number of DoFs to be managed by controlling groups of joints or muscles, referred to as spatial modules, rather than single joints or muscles separately. Accordingly, the time-varying motor commands sent to the spatial modules are referred to as temporal modules.How the brain constructs various repertoires of motions is another fundamental question. One possible solution is that the brain modulates the spatiotemporal modules depending on the task at hand. On the one hand, temporal modules, rather than spatial modules, may be modulated for specific tasks, such as walking, running, executing various types of gaits, or responding to unpredictable perturbations while walking 7,8 . On the other hand, task-dependent modulation may be applied to spatial modules rather than to temporal modules for certain tasks, such as responding to unpredictable perturbations to maintain bal...

Section: Discussionmentioning

confidence: 99%

Speed-dependent and mode-dependent modulations of spatiotemporal modules in human locomotion extracted via tensor decomposition

Yokoyama

Kaneko

et al. 2020

Sci Rep

Self Cite

“…In these analyses, however, we need to pay attention to that the tensor decomposition is an unsupervised method; the extracted spatiotemporal modules are not directly related to those gait parameters. In other words, we can extract the dimensions inherent to the joint angles or EMG data as more relevant to those gait parameters using supervised methods such as regression and classification [28]. Although it is possible to relate each spatiotemporal module to some gait parameters, we need to remember that the relation is indirect.…”

Section: Discussionmentioning

confidence: 99%

“…For extracting such error-correction-related modules, the tensor decomposition can be effectively used after being combined with regression frameworks [38]. A regression framework enables us to extract the hidden information relevant to target values [28, 39]. Because several studies have focused on adaptation [35–37, 40–43] and spatiotemporal modules [2–6, 11] separately, the relation between those concepts has only been investigated in a few studies [10, 16, 28].…”

Section: Discussionmentioning

confidence: 99%

Detecting task-dependent modulation of spatiotemporal module via tensor decomposition: application to kinematics and EMG data for walking and running at various speed

Yokoyama

Kaneko

et al. 2019

Preprint

Self Cite

1How the central nervous system (CNS) controls many joints and muscles is a fundamental question 2 in motor neuroscience and related research areas. An attractive hypothesis is the module hypothesis: 3 the CNS controls groups of joints or muscles (i.e., spatial modules) while providing time-varying motor 4 commands (i.e., temporal modules) to the spatial modules rather than controlling each joint or muscle 5 separately. Another fundamental question is how the CNS generates numerous repertories of movement 6 patterns. One hypothesis is that the CNS modulates the spatial and/or temporal modules depending 7 on the required tasks. It is thus essential to quantify the spatial module, the temporal module, and 8 the task-dependent modulation of those modules. Although previous methods attempted to quantify 9 these aspects, they considered the modulation in only the spatial or temporal module. These limitations 10 were possibly due to the constraints inherent to conventional methods for quantifying the spatial and 11 temporal modules. Here, we demonstrate the effectiveness of tensor decomposition in quantifying the 12 spatial module, the temporal module, and the task-dependent modulation of these modules without such 13 limitations. We further demonstrate that the tensor decomposition provides a new perspective on the 14 task-dependent modulation of spatiotemporal modules: in switching from walking to running, the CNS 15 modulates the peak timing in the temporal module while recruiting proximal muscles in the corresponding 16 spatial module. 17Author summary 18 There are at least two fundamental questions in motor neuroscience and related research areas: 1) how 19 does the central nervous system (CNS) control many joints and muscles and 2) how does the CNS 20 generate numerous repertories of movement patterns. One possible answer to question 1) is that the 21 CNS controls groups of joints or muscles (i.e., spatial modules) while providing time-varying motor 22 factors inherent to joint angle and electromyographic (EMG) data (i.e., spatial and temporal modules), 54 there can be limitations to considering more than three factors such as spatial modules, temporal modules, 55 and the task-dependent modulations of those modules. For example, with matrix decomposition, the task-56 dependent modulation of the temporal module between two tasks has been discussed under the constraint 57 of the same spatial module; on the other hand, the modulation of the spatial module has been discussed 58 without considering the temporal module. These constraints can thus provide diverse and non-unified 59 perspectives on the task-dependent modulation of the spatiotemporal modules. 60Here, we demonstrate the effectiveness of tensor decomposition in extracting the spatial module, the 61 temporal module, and the task-dependent modulations of the spatiotemporal modules. Tensor decompo-62 sition is a generalized version of matrix decomposition. By constructing tensor data that combine matrix 63 data in the third dimension, tensor decomposition e...

“…While the UCM focuses on kinematic outcome (e.g., the position of hand or center of mass), the goal equivalent manifold (GEM) 3 and noise-tolerant-covariance (TNC) 4 methods quantify the task-relevant and task-irrelevant motion components by explicitly defining the relations between kinematic parameters and task outcome (e.g., in ball throwing, the height of the ball flight can be approximated by parabolic motion). In addition to the GEM and the TNC, our recent methods detect the task-relevant and task-irrelevant motion components after estimating the unknown relation between time-varying motion and task outcome in a data-driven manner 5,6 .…”

Section: Decomposition Of Motion Data Into Task-relevant and Task-irrmentioning

confidence: 99%

A data-driven approach to decompose motion data into task-relevant and task-irrelevant components in categorical outcome

Furuki

2020

Sci Rep

Self Cite

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.