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相关概念视频

Machines: Problem Solving I01:22

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

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Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease
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使用嵌入式机器学习进行步行步伐长度估计.

Joeri R Verbiest1,2, Bruno Bonnechère2,3, Wim Saeys4

  • 1Department of Sciences and Technology, Karel de Grote (KdG) University of Applied Sciences and Arts, 2660 Antwerp, Belgium.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

这项研究开发了嵌入式机器学习模型,用于估计微控制器上的步伐步伐长度. int8模型以最小的内存实现了高精度,从而实现了设备上的步态分析.

关键词:
在IMU,IMU是IMU.这是一个MCU.嵌入式机器学习 嵌入式机器学习步态分析 步态分析步行方式 步伐长度 步伐长度医疗保健 医疗保健 医疗保健 医疗保健惯性测量单位是一种惯性测量单位.机器学习是机器学习.微控制器上的微控制器神经网络的神经网络的神经网络这是一个回归回归的回归.tiny tinyMLML微小的MLML是什么意思可以穿戴的传感器.

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科学领域:

  • 生物医学工程 生物医学工程
  • 可穿戴技术可穿戴技术
  • 机器学习 机器学习

背景情况:

  • 传统的步态分析依赖于基于实验室的仪器仪表.
  • 现有的可穿戴步态评估设备资源有限,限制了设备上的机器学习.
  • 嵌入式机器学习 (tinyML) 为微控制器上的处理提供了一个解决方案.

研究的目的:

  • 开发一个机器学习模型用于步行步伐长度估计.
  • 创建一个可以在资源有限的微控制器上部署的模型.
  • 通过可穿戴传感器实现直接在设备上进行步态分析.

主要方法:

  • 使用了来自15名健康个体的4467步步的数据集.
  • 开发了一个多层1D卷积神经网络 (CNN).
  • 使用MLOps工具创建了float32和int8精度模型.

主要成果:

  • int8模型实现了0.07±4.3厘米的平均准确度和精度.
  • int8型号只需要91.6kB的闪存和13.6kB的RAM.
  • 这两种模型都在Cortex-M4F微控制器上成功部署.

结论:

  • 直接在微控制器上估计步伐步伐长度是可行的.
  • 嵌入式机器学习 (tinyML) 对可穿戴步态分析设备具有重大潜力.
  • 这种方法提高了资源有限的可穿戴传感器的能力.