一个可解释的实时传感器图形变压器用于舞蹈识别
Jinying Han1,2, Shan Wang3, Jiayin Gao2,4
1College of Music and Dance, Guangzhou University, Guangzhou, 510006, China.
Scientific reports
|January 14, 2026
概括
这项研究提出了一个可解释的AI框架,用于使用传感器数据进行舞蹈识别. 该系统实现了高精度和实时性能,即使具有具有挑战性的舞蹈和传感器变化.
科学领域:
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
背景情况:
- 从传感器数据中自动解释舞蹈运动是具有挑战性的,因为时间忠实性,方向不变性和实时约束.
- 现有的方法与多样化的舞蹈编排,传感器放置和表演者动力学作斗争,提供有限的解释能力.
研究的目的:
- 为实时,边缘级部署引入一个可解释的,传感器驱动的舞蹈识别框架.
- 为了提高准确性,并为舞蹈运动分析提供可验证的推理.
主要方法:
- 开发了一个自适应式传感器规范化模块,具有基于四次元的定向校正和卡尔曼改进.
- 实现了一个多尺度运动特征提取器,使用节奏条件扩张进行节奏结构分析.
- 使用一个空间时间图注意力核心,图形卷曲和双重注意力用于传感器突出度和时间度.
主要成果:
- 在惯性数据集上实现了高达94.2%的分类准确度和92.8%的运动质量估计.
- 证明每延迟时间低于8.5毫秒,在节拍变化,传感器漂移和频道损失下确认稳定性.
- 优化用于边缘类执行,使用内核级压缩和因果注意窗口.
结论:
- 拟议的框架提供了强大的和可解释的舞蹈识别,克服了现有方法的局限性.
- 该系统的实时性能和准确性使其适用于受限制的硬件和动态环境.
- 可解释的决策提供了对类特定运动决定因素的洞察,提高了模型的透明度.
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