运动解码 (LocoD):一个开源和模块化平台,用于研究下肢辅助设备的控制算法
Bahareh Ahkami1,2, Kirstin Ahmed1,3,4,5, Morten B Kristoffersen1,6,7
1Center for Bionics and Pain Research, Gothenburg, Sweden.
Applied bionics and biomechanics
|January 7, 2026
概括
我们开发了LocoD,这是一个开源软件平台,用于解码假肢控制,使用生物电信号,如EMG. 将EMG与非生物传感器相结合,可以显著提高运动模式预测的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 康复技术 康复技术 康复技术
- 信号处理 信号处理
背景情况:
- 目前的机动假肢依赖于非生物信号,限制了自然控制.
- 从残余四肢解码生物信号 (例如,EMG) 提供了更直观的假肢控制的潜力.
- 缺乏标准化的方法阻碍了对生物电信号解码算法的研究.
研究的目的:
- 推出LocoD,一个开源软件平台,用于统一记录和处理生物电气 (EMG) 和非生物传感器数据.
- 为了使对假肢应用的控制算法进行研究和基准测试.
- 为了验证平台在解码运动模式中的功能.
主要方法:
- 开发了LocoD,这是一个开源平台,用于EMG和非生物信号处理 (预处理,特征提取,分类).
- 记录了来自21名健身参与者的EMG,IMU和压力传感器数据,这些数据来自各种运动任务 (步行,楼梯,坡道) 的21名健身参与者.
- 通过使用三个传感器组合来比较移动模式的分类准确性:EMG+IMU+压力传感器,单独使用EMG,单独使用IMU+压力传感器.
主要成果:
- 在EMG,IMU和压力传感器的组合中,实现了最高的运动模式预测精度 (93.4%±3.9%).
- 单独使用EMG的准确性较低 (74.56%±5.8%),而IMU+压力传感器的准确性为90.77%±4.6%.
- 综合传感器方法的优势在统计学上显著 (p < 0.001).
结论:
- LocoD是一个经过验证的,开源的,模块化的平台,用于研究假肢控制算法.
- 该平台促进了生物电和非生物信号的整合和分析.
- 研究结果表明,用于假肢控制的综合传感器方法的准确性提高了.
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