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

Decomposition of motion data into task-relevant and task-irrelevant components is an effective way to clarify the diverse features involved in motor control and learning. Several previous methods have succeeded in this type of decomposition while focusing on the clear relation of motion to both a specific goal and a continuous outcome, such as a 10 mm deviation from a target or 1 m/s hand velocity. In daily life, it is vital to quantify not only continuous but also categorical outcomes. For example, in baseball, batters must judge whether the opposing pitcher will throw a fastball or a breaking ball; tennis players must decide whether an opposing player will serve out wide or down the middle. However, few methods have focused on quantifying categorical outcome; thus, how to decompose motion data into task-relevant and task-irrelevant components when the outcome is categorical rather than continuous remains unclear. Here, we propose a data-driven method to decompose motion data into task-relevant and task-irrelevant components when the outcome takes categorical values. We applied our method to experimental data where subjects were required to throw fastballs or breaking balls with a similar form. Our data-driven approach can be applied to the unclear relation between motion and outcome, and the relation can be estimated in a data-driven manner. Furthermore, our method can successfully evaluate how the task-relevant components are modulated depending on the task requirements.

[1]  Masaya Hirashima,et al.  Prospective errors determine motor learning , 2015, Nature Communications.

[2]  Masato Okada,et al.  Statistical method for detecting phase shifts in alpha rhythm from human electroencephalogram data. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Dagmar Sternad,et al.  A randomization method for the calculation of covariation in multiple nonlinear relations: illustrated with the example of goal-directed movements , 2003, Biological Cybernetics.

[4]  Paola Cesari,et al.  Body-goal Variability Mapping in an Aiming Task , 2006, Biological Cybernetics.

[5]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[6]  N. A. Bernshteĭn The co-ordination and regulation of movements , 1967 .

[7]  Ken Takiyama,et al.  Balanced motor primitive can explain generalization of motor learning effects between unimanual and bimanual movements , 2016, Scientific reports.

[8]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[9]  Masato Okada,et al.  Exact Inference in Discontinuous Firing Rate Estimation Using Belief Propagation , 2009 .

[10]  Ken Takiyama,et al.  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 , 2019, Scientific Reports.

[11]  Gregor Schöner,et al.  The uncontrolled manifold concept: identifying control variables for a functional task , 1999, Experimental Brain Research.

[12]  Masato Okada,et al.  Detection of Hidden Structures in Nonstationary Spike Trains , 2011, Neural Computation.

[13]  Angkoon Phinyomark,et al.  Kinematic gait patterns in healthy runners: A hierarchical cluster analysis. , 2015, Journal of biomechanics.

[14]  Madalina Fiterau,et al.  Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. , 2018, Journal of biomechanics.

[15]  Ken Takiyama,et al.  Context-dependent memory decay is evidence of effort minimization in motor learning: a computational study , 2015, Front. Comput. Neurosci..

[16]  Ken Takiyama,et al.  Development of a Portable Motor Learning Laboratory (PoMLab) , 2016, PloS one.

[17]  Maja Pohar Perme,et al.  Comparison of logistic regression and linear discriminant analysis , 2004, Advances in Methodology and Statistics.

[18]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[19]  Ken Takiyama,et al.  Detecting the relevance to performance of whole-body movements , 2017, Scientific Reports.

[20]  Richard Kempter,et al.  State-dependencies of learning across brain scales , 2015, Front. Comput. Neurosci..

[21]  Ken Takiyama,et al.  Influence of switching rule on motor learning , 2018, Scientific Reports.

[22]  Reza Shadmehr,et al.  Learning of action through adaptive combination of motor primitives , 2000, Nature.

[23]  David W. Hosmer,et al.  Applied logistic regression : solutions manual to accompany , 2001 .