activity
20242026
most citedPassAI: explainable artificial intelligence algorithm for soccer pass analysis using multimodal information resources

5 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Machine learning modeling of hit-into-play probability and quantification of pitch sequence effects

Ryota Takamido, Hiroki Nakamoto

Although pitch sequencing is a central topic in baseball analytics, previous studies have primarily focused on improving predictive performance for the final pitch or its outcome,…

cs.HC2026

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

Ryota Takamido, Chiharu Suzuki, Hiroki Nakamoto

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 cr…

cs.HC2026

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

Ryota Takamido, Chiharu Suzuki, Hiroki Nakamoto

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 valuabl…

cs.HC2025★ 1 cited

Personalized Motion Guidance Framework for Athlete-Centric Coaching

Ryota Takamido, Chiharu Suzuki, Hiroki Nakamoto

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 ne…

cs.HC2025★ 5 cited

PassAI: explainable artificial intelligence algorithm for soccer pass analysis using multimodal information resources

Ryota Takamido, Jun Ota, Hiroki Nakamoto

This study developed a new explainable artificial intelligence algorithm called PassAI, which classifies successful or failed passes in a soccer game and explains its rationale usi…

cs.HC2024★ 3 cited

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

Ryota Takamido, Chiharu Suzuki, Jun Ota +1

Background: Simultaneously focusing on intra- and inter-individual body dynamics and elucidating how these affect each other will help understand human inter-personal coordination…