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

IF 3.5 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Frontiers in Neurorobotics Pub Date : 2026-02-06 eCollection Date: 2026-01-01 DOI:10.3389/fnbot.2026.1760494

Xiaoxiao Cao

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引用次数: 0

Abstract

Introduction: The integration of virtual simulation with intelligent modeling is crucial for advancing the scientization and personalization of volleyball physical training. This study aims to overcome the convergence instability and feature misalignment in modeling multimodal kinematic and physiological sequences.

Methods: A dynamical framework based on a Dual-Stream Long Short-Term Memory network integrated with a temporal attention mechanism is proposed. The framework decouples heterogeneous feature learning and optimizes temporal weight distribution.

Results: Experimental validation on complex motion state estimation demonstrates that the proposed model reduces load modeling error to 3.8% and achieves a motion classification accuracy of 93.1%. The velocity trajectory fitting coefficient of determination is 0.91 with a peak deviation of 0.05 m/s.

Discussion: 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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复杂生理状态估计双流LSTM网络的多模态序列动力学及收敛优化。

虚拟仿真与智能建模相结合是推进排球体能训练科学化、个性化的关键。该研究旨在克服多模态运动和生理序列建模中的收敛不稳定性和特征不对准问题。方法：提出了一种基于双流长短期记忆网络和时间注意机制的动态框架。该框架解耦了异构特征学习，优化了时间权重分布。结果：对复杂运动状态估计的实验验证表明，该模型将负载建模误差降低到3.8%，运动分类准确率达到93.1%。确定的速度轨迹拟合系数为0.91，峰值偏差为0.05 m/s。讨论：这些结果证实了基于注意力的DS-LSTM在优化用于训练状态估计和反馈的多模态序列建模方面的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS

CiteScore

5.20

自引率

6.50%

发文量

250

审稿时长

14 weeks

期刊介绍： Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.