5 citations · 9 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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…