Suppr超能文献

可视编程助力可访问的交互式肌肉骨骼模型。

Visual programming for accessible interactive musculoskeletal models.

机构信息

Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, 360 Huntington Avenue, 301 Robinson Hall, Boston, MA, 02115-5005, USA.

Department of Bioengineering, Northeastern University, Boston, USA.

出版信息

BMC Res Notes. 2022 Mar 22;15(1):108. doi: 10.1186/s13104-022-05994-5.

Abstract

OBJECTIVE

Musculoskeletal modeling and simulation are powerful research and education tools in engineering, neuroscience, and rehabilitation. Interactive musculoskeletal models (IMMs) can be controlled by muscle activity recorded with electromyography (EMG). IMMs are typically coded using textual programming languages that present barriers to understanding for non-experts. The goal of this project was to use a visual programming language (Simulink) to create and test an IMM that is accessible to non-specialists for research and educational purposes.

RESULTS

The developed IMM allows users to practice a goal-directed task with different control modes (keyboard, mouse, and EMG) and actuator types (muscle model, force generator, and torque generator). Example data were collected using both keyboard and EMG control. One male participant in his early 40's performed a goal-directed task for four sequential trials using each control mode. For EMG control, the participant used a low-cost EMG system with consumer-grade EMG sensors and an Arduino microprocessor. The participant successfully performed the task with both control modes, but the inability to grade muscle model excitation and co-activate antagonist muscles limited performance with keyboard control. The IMM developed for this project serves as a foundation that can be further tailored to specific research and education needs.

摘要

目的

肌肉骨骼建模和仿真在工程学、神经科学和康复领域是强大的研究和教育工具。交互式肌肉骨骼模型(IMM)可以通过肌电图(EMG)记录的肌肉活动来控制。IMM 通常使用文本编程语言进行编码,这对非专业人士来说理解起来存在障碍。本项目的目标是使用可视化编程语言(Simulink)创建和测试一种非专业人士可用于研究和教育目的的 IMM。

结果

所开发的 IMM 允许用户使用不同的控制模式(键盘、鼠标和 EMG)和执行器类型(肌肉模型、力发生器和扭矩发生器)来练习目标导向任务。使用键盘和 EMG 控制收集了示例数据。一名 40 岁出头的男性参与者使用每种控制模式完成了四个连续试验的目标导向任务。对于 EMG 控制,参与者使用了带有消费级 EMG 传感器和 Arduino 微处理器的低成本 EMG 系统。参与者成功地使用了两种控制模式完成了任务,但由于无法对肌肉模型的激励进行评分以及共同激活拮抗肌,因此使用键盘控制的表现受到限制。本项目开发的 IMM 作为一个基础,可以根据特定的研究和教育需求进一步定制。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验