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arXiv AI By Nipa Anjum, Md Irfan Pavel, Robert Gonzalez Jr, Kevin Desai, Alberto Cordova, M. Rasel Mahmud, John Quarles

Toward Postural State Classification in Immersive VR with Multimodal Data and Explainability Analysis

Read the original on arXiv AI →

This study evaluates machine learning and deep‑learning models for classifying balanced versus imbalanced postural states in immersive virtual reality using a multimodal dataset of kinematic, EMG, and EDA signals. The Mamba‑inspired CNN (MI‑CNN) achieved the highest accuracy (96.76%) and, through SHapley Additive exPlanations (SHAP), identified kinematic features as the most influential for detecting imbalance. Even after reducing input dimensionality by 33% based on SHAP importance, the model maintained near‑optimal performance (0.957 accuracy and F1‑score).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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