Multimodal sequence dynamics and convergence optimization in dual-stream LSTM networks for complex physiological state estimation

These results confirm the effectiveness of the attention-based DS-LSTM in optimizing multimodal sequence modeling for training state estimation and feedback.

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Publication Details

Journal
Frontiers in Neurorobotics
Published
2026-02-06
DOI
https://doi.org/10.3389/fnbot.2026.1760494
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
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article

Multimodal sequence dynamics and convergence optimization in dual-stream LSTM networks for complex physiological state estimation

Xiaoxiao Cao
Frontiers in Neurorobotics
Sports Performance and Training
article

Multimodal sequence dynamics and convergence optimization in dual-stream LSTM networks for complex physiological state estimation

Xiaoxiao Cao
article en

Abstract

These results confirm the effectiveness of the attention-based DS-LSTM in optimizing multimodal sequence modeling for training state estimation and feedback.

Frontiers in NeuroroboticsVol. 20
China Academy of Art (CN)
Openalex Percentile: Top 68%
Sports Performance and Training
DOIOpen Access PDFOpenAlex
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Multimodal sequence dynamics and convergence optimization in dual-stream LSTM networks for complex physiological state estimation — Xiaoxiao Cao · Frontiers in Neurorobotics (2026) | TGRS Research Map | TGRS