Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Machines: Problem Solving II01:30

Machines: Problem Solving II

271
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.
271

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Automated Assessment of PPE Compliance with Fast Lightweight Deep Learning Based Computer Vision.

IISE transactions on occupational ergonomics and human factors·2026
Same author

Multimodal prediction of situation awareness during automated driving: a gaze and EEG-based approach.

Ergonomics·2025
Same author

Objective and Subjective Evaluation of Non-Technical Skills and Technical Leadership Skills During Simulated Critical Care Scenarios.

IISE transactions on occupational ergonomics and human factors·2025
Same author

Building a Realistic Virtual Luge Experience Using Photogrammetry.

Sensors (Basel, Switzerland)·2025
Same author

A Single-Camera Method for Estimating Lift Asymmetry Angles Using Deep Learning Computer Vision Algorithms.

IEEE transactions on human-machine systems·2025
Same author

Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm.

Sensors (Basel, Switzerland)·2025

相关实验视频

Updated: May 17, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

309

触觉手套预测提升时的负载重量,使用深度神经网络进行提升.

Guoyang Zhou1, Ming-Lun Lu2, Denny Yu1

  • 1School of Industrial Engineering, Purdue University, West Lafayette, IN 47906 USA.

IEEE sensors journal
|March 31, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了触摸手套,用于在举重任务中估计负载重量,减少职业伤害. 开发的ResNet 18模型使用手压数据准确预测重量,为提升力学提供了新的见解.

关键词:
负载重量预测预测神经网络的神经网络的神经网络触觉手套是一种触觉手套.

更多相关视频

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.4K
Measurement of Spatial Stability in Precision Grip
09:36

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

3.1K

相关实验视频

Last Updated: May 17, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

309
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.4K
Measurement of Spatial Stability in Precision Grip
09:36

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

3.1K

科学领域:

  • 职业安全 在职业安全.
  • 生物力学 生物力学
  • 机器学习 机器学习

背景情况:

  • 举重时过度劳累是造成工作场所伤害的主要原因之一.
  • 准确的负载重量估计对于评估提升任务风险至关重要.
  • 目前用于估计负载重量的方法通常是间接的或需要侵入性传感器.

研究的目的:

  • 提出触摸手套作为一种新的方法,用于预测提升时的负载重量.
  • 评估深度神经网络在从触觉数据估计负载重量的性能.
  • 通过可解释性技术,分析提升过程中的手力施加模式.

主要方法:

  • 触摸手套用于收集提升任务期间的手压数据.
  • 收集的数据被编制成一个2D矩阵,捕获空间和时间信息.
  • 使用ResNet 18深度神经网络回归模型进行负载重量预测.
  • 沙普利增量解释 (SHAPs) 用于解释模型决策并确定关键特征.

主要成果:

  • ResNet 18模型实现了0.821的预测R平方和1.579公斤的平均绝对误差.
  • SHAP的分析显示,右手,手指和中部提升阶段对体重预测最重要.
  • 该研究证明了使用触觉感应来估计载荷重量的可行性.

结论:

  • 触觉手套提供了一种可行的,非侵入性的方法,用于预测提升任务中的负载重量.
  • 这些发现为我们提供了有价值的见解,了解如何在提升过程中施加手力.
  • 这项技术有可能通过改进手动处理的风险评估来提高职业安全.