越过地面障碍物时绊倒风险影响因素分析
Ergonomics(2023)SCI 2区SCI 3区
Abstract
目的 探讨地面障碍物深度、高度及行走者步频对越过障碍时绊倒风险,旨在减少劳动场所职业跌倒伤害.方法 设计3因子步态实验,3因子包括障碍深度(5和10 cm)、障碍高度(1,5和15 cm)和步频(95和125 step/min).实验招募共计8名男性大学生作为受测者,运用Vicon动作捕捉系统收集不同步频下,越过不同深度及高度障碍时受测者的步态数据,并对其进行统计分析.结果 障碍深度对越过障碍时脚到障碍最小间隙(MFCs)没有显著的影响(P>0.05),障碍高度对MFCs具有显著的影响(P<0.0001),越过障碍时步长受步频的影响是显著的(P<0.05),建立MFCs与步长、实验3因子的线性逐步回归模型.结论 越过障碍时可以不考虑障碍深度的影响,随着障碍高度的增大、步频的加快,绊倒风险不断增大;采取增加抬脚高度、增大步长的策略可以减小越过障碍时的绊倒风险.本研究为控制越过地面障碍时绊倒风险,减少跌倒伤害提供理论参考.
MoreKey words
occupntion henlth,fall injury,accident prevention,obstacle height,obstacle depth,walking frequen-cy,crossing an obstacle,the risk of tripping,production safety
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper