arXiv subjects

Chiharu Suzuki

Publications and source records attributed to Chiharu Suzuki.

4 recordsLinked to original sources

arXiv2026

Cross-individual generalizability of machine learning models for ball speed prediction in baseball pitching

Although machine learning (ML)-based performance outcome prediction is an important topic in contemporary sports science, one important issue is the limited understanding of the cross-individual generalizability of ML models in sports contexts. To address this issue, this study aimed to evaluate the cross-individual generalizability of ML models for predicting ball speed in baseball pitching. A dataset comprising 50 pitchers from various competitive levels was analyzed. Cross-individual generalizability was assessed using leave-one-subject-out cross-validation. Specifically, the effects of expertise level and restrictions on spatiotemporal motion information were examined to identify factors influencing model generalizability. The results revealed that, under cross-individual evaluation, (1) predictive performance was markedly lower than under within-individual evaluation, with R-squared value decreasing from 0.91 to 0.38; (2) the model tended to overestimate the performance of Intermediate pitchers relative to Expert pitchers, with a significant group difference in signed prediction error (p < .05); and (3) the trunk and pivot leg demonstrated relatively high generalization performance, with the pivot leg showing notable generalizability even during the weight-shift initiation phase (R-squared value > 0.25). These findings underscore the importance of cross-individual evaluation in enhancing the practical applicability of ML in sports settings and contribute to a deeper understanding of the biomechanical factors underlying the target movement.

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arXiv2026

Personalized Motion Guidance Framework for Athlete-Centric Coaching

A critical challenge in contemporary sports science lies in filling the gap between group-level insights derived from controlled hypothesis-driven experiments and the real-world need for personalized coaching tailored to individual athletes' unique movement patterns. This study developed a Personalized Motion Guidance Framework (PMGF) to enhance athletic performance by generating individualized motion-refinement guides using generative artificial intelligence techniques. PMGF leverages a vertical autoencoder to encode motion sequences into athlete-specific latent representations, which can then be directly manipulated to generate meaningful guidance motions. Two manipulation strategies were explored: (1) smooth interpolation between the learner's motion and a target (e.g., expert) motion to facilitate observational learning, and (2) shifting the motion pattern in an optimal direction in the latent space using a local optimization technique. The results of the validation experiment with data from 51 baseball pitchers revealed that (1) PMGF successfully generated smooth transitions in motion patterns between individuals across all 1,275 pitcher pairs, and (2) the features significantly altered through PMGF manipulations reflected known performance-enhancing characteristics, such as increased stride length and knee extension associated with higher ball velocity, indicating that PMGF induces biomechanically plausible improvements. We propose a future extension called general-PMGF to enhance the applicability of this framework. This extension incorporates bodily, environmental, and task constraints into the generation process, aiming to provide more realistic and versatile guidance across diverse sports contexts.

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arXiv2026

Data-driven modelling of low-dimensional dynamical structures underlying complex full-body human movement

One of the central challenges in the study of human motor control and learning is the degrees-of-freedom problem. Although the dynamical systems approach (DSA) has provided valuable insights into addressing this issue, its application has largely been confined to cyclic or simplified motor movements. To overcome this limitation, the present study employs neural ordinary differential equations (NODEs) to model the time evolution of non-cyclic full-body movements as a low-dimensional latent dynamical system. Given the temporal complexity full-body kinematic chains, baseball pitching was selected as a representative target movement to examine whether DSA could be extended to more complex, ecologically valid human movements. Results of the verification experiment demonstrated that the time evolution of a complex pitching motion could be accurately predicted (R^2 > 0.45) using the NODE-based dynamical model. Notably, approximately 50% of the variance in the latter half of the pitching motion was explained using only the initial ~8% of the temporal sequence, underscoring how subsequent movement evolves from initial conditions according to ODE-defined dynamics in latent space. These findings indicate the potential to extend the DSA to more complex and ecologically valid forms of human movement.

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arXiv2024

Understanding whole-body inter-personal dynamics between two players using neural Granger causality as the explainable AI (XAI)

Background: Simultaneously focusing on intra- and inter-individual body dynamics and elucidating how these affect each other will help understand human inter-personal coordination behavior. However, this association has not been investigated previously owing to difficulties in analyzing complex causal relations among several body components.To address this issue, this study proposes a new analytical framework that attempts to understand the underlying causal structures behind each joint movement of individual baseball players using neural Granger causality (NGC) as the explainable AI. Methods: In the NGC analysis, causal relationships were defined as the size of the weight parameters of the first layer of a machine-learning model trained to predict the future state of a specific time-series variable. To verify the approach in a practical context, we conducted an experiment with 16 pairs of expert baseball pitchers and batters; input datasets with 27 joint resultant velocity data (joints of 13 pitchers and 14 batters) were generated and used for model training.Results: NGC analysis revealed significant causal relations among intra- and inter-individual body components such as the batter's hands having a causal effect from the pitcher's throwing arm. Remarkably, although the causality from the batter's body to pitcher's body is much lower than the reverse, it is significantly correlated with batter performance outcomes. Conclusions: The above results suggest the effectiveness of NGC analysis for understanding whole-body inter-personal coordination dynamics and that of the AI technique as a new approach for analyzing complex human behavior from a different perspective than conventional techniques.

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