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相关概念视频

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

460
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
460

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相关实验视频

Updated: Jun 30, 2025

Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform
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奥里图斯:一个开源优化工具包,用于训练和开发使用可读物的人类运动模型和过器.

Swapnil Sayan Saha1, Sandeep Singh Sandha1, Siyou Pei1

  • 1University of California - Los Angeles, USA.

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies
|March 22, 2024
PubMed
概括

Auritus是一个开源工具包,通过提供数据收集工具和优化机器学习模型来简化可听应用程序的开发. 它可以在资源有限的设备上高精度地实时评估人类活动和头部姿势.

关键词:
在TinyML中使用TinyML.数据集数据集数据集.这是一个可听的耳机.过器 过器 过器在循环中的硬件.头部姿势 - 头部姿势人类活动 人类活动机器学习是机器学习.网络架构 搜索 搜索 搜索神经网络的神经网络的神经网络优化的优化优化优化.

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相关实验视频

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科学领域:

  • 可穿戴技术可穿戴技术
  • 机器学习 机器学习
  • 人与计算机的交互

背景情况:

  • 耳戴式设备 (可听设备) 正在发展成为具有内置运动传感器和生命信号分析的多模式接口.
  • 开发用于可听设备的感觉应用程序是具有挑战性的,因为缺乏大规模的开源数据集和设备的计算约束.
  • 现有的人类活动和头部姿势估计算法很难在可听设备上集成,因为它们的尺寸和处理能力有限.

研究的目的:

  • 介绍Auritus,一个开源优化工具包,旨在克服开发可听传感应用程序的障碍.
  • 为了促进数据收集,预处理和标签定制可听数据集.
  • 为可听设备开发轻量级的实时机器学习模型.

主要方法:

  • 奥里图斯提供了用于数据收集,预处理和标签的图形工具,以及243万个惯性样本的开源数据集.
  • 该工具包集成了硬件在循环 (HIL) 优化器和TinyML接口,用于创建高效的机器学习模型.
  • 该系统使用用于摔倒检测,空间音频染和增强现实 (AR) 接口的应用程序进行了验证.

主要成果:

  • 通过使用6-13kB的实时模型,Auritus在活动识别方面实现了91%的离开一次测试准确率 (98%的测试准确率).
  • 开发的模型比最先进的方法要小得多 (98-740x),并且比最先进的方法更准确 (3-6%).
  • 使用20kB的过器,头部姿势估计实现了低绝对误差 (低至5度),精度提高了1.6倍.

结论:

  • 通过简化数据处理和优化机器学习模型,Auritus有效地解决了开发可听的应用程序的挑战.
  • 奥里图斯的开源性质鼓励社区贡献,并为研究人员和开发人员提供快速原型.
  • 该工具包展示了在资源有限的可听设备上部署准确和高效的感觉应用程序的可行性.