English(EN) Pose2Muscle: Structured Spatio-Temporal Decoding for Discrete Muscle Activity Estimation from Human Pose

Pose2Muscle框架从人体姿态估计肌肉活动

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了Pose2Muscle，一个新颖的框架，旨在从人体姿态数据中估计离散肌肉活动，而无需肌电图（sEMG）等专用传感器。该方法将肌肉估计重新构建为结构化预测问题，侧重于离散肌肉状态而非连续sEMG信号，以提高稳定性和可解释性。该框架利用多尺度时空注意力和基于有向无环图的解码器来推断肌肉活动模式。为了促进这项研究，创建了一个名为PoseEMG-43的新同步姿态-sEMG数据集，该数据集包含43种日常生活动作的2,992个运动实例。 AI

影响为康复和损伤预防应用中的非侵入性肌肉活动分析提供了可能。

排序理由该集群包含一篇学术论文，详细介绍了一种从人体姿态估计肌肉活动的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了一种从人体姿态估计肌肉活动的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Yuepeng Chen, Jiehong Shi, Kaili Zheng, Boyi Zhang, Chenyi Guo, Ji Wu, Xiangling Fu · 2026-09-17 04:00

Pose2Muscle：从人体姿态估计离散肌肉活动结构化时空解码

arXiv:2609.18336v1 Announce Type: new Abstract: Muscle activity is fundamental to human movement, and understanding its patterns is critical for injury prevention and rehabilitation. Conventional muscle activity monitoring relies on specialized sensors such as surface electromyog…

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报道来源 [1]

Pose2Muscle：从人体姿态估计离散肌肉活动结构化时空解码

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