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Updated: Jan 9, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

基于异步BLE 5.0 IMU传感器网络的计算机模型用于生物机械应用.

Juan Antonio Mora-Sánchez¹, Luis Pastor Sánchez-Fernández¹, Diana Lizet González-Baldovinos¹

¹Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz S/N, Nueva Industrial Vallejo, Gustavo A. Madero, Mexico City 07738, Mexico.

Sensors (Basel, Switzerland)

|December 11, 2025

概括

这项研究引入了一个新的无线传感器网络,使用惯性测量单元 (IMU) 和蓝牙低能 (BLE) 5.0进行准确的生物力学数据收集. 该系统增强了实时运动捕捉和职业健康评估.

关键词:

蓝牙低能耗 5.0 版本国际海事统筹单位 (IMU) 是一个国际海事统筹单位.异步数据采集数据采集一个模糊的推断系统.快速上肢评估快速上肢评估

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A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

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Published on: April 12, 2016

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

相关实验视频

Last Updated: Jan 9, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

科学领域:

生物力学生物力学
传感器网络传感器网络
人与计算机的交互

背景情况:

动态环境需要先进的传感器网络来获取生物机械数据.
现有系统在范围,速度和稳定性方面面临限制.

研究的目的:

使用惯性测量单位 (IMU) 开发和验证一个强大的异步网络架构.
使用蓝牙低能耗 (BLE) 5.0来克服之前的实施限制.
实现实时生物机械信号采集,以增强运动捕捉和健康评估.

主要方法:

实施了六个IMU的网络,使用混合Python 3.10-LabVIEW 2022 Q3框架.
通过虚拟端口模拟来确保并发,异步的数据采集,以实现稳定的传感器互连.
通过75次评估验证了系统性能,涉及25名参与者进行姿势实验.

主要成果:

实现了高采集效率 (近100%),数据损失低于±2%.
展示了40米的最大室内范围和105米的户外范围.
在一个案例研究中成功地应用了该系统,使用了用于快速上肢评估 (RULA) 的模糊推理系统 (FIS).

结论:

开发的架构为移动捕捉应用提供了卓越的可扩展性和稳定性.
该系统为职业健康提供客观,可量化的指标,特别是对于坐着的工作.
克服了观察方法在评估受伤风险和上肢姿势方面的局限性.