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评估用于推断跑步机运行运动的稀疏惯性测量单元配置

Mackenzie N Pitts¹, Megan R Ebers², Cristine E Agresta³

¹Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.

Sensors (Basel, Switzerland)

|April 12, 2025

概括

浅反复解码器网络 (SHRED) 可以从单个惯性测量单元 (IMU) 传感器中重建密集的运行数据. 这种方法准确地推断信号,可能会扩大使用更少传感器的运动分析.

科学领域:

生物力学生物力学
运动科学运动科学运动科学
可穿戴技术可穿戴技术

背景情况:

惯性测量单位 (IMU) 对于分析运行性能至关重要.
有限的传感器数据 (稀疏性) 限制了数字生物标志物的评估.
浅反复解码器网络 (SHRED) 可以从单个传感器中重建密集的时间序列信号,显示出对人类移动性分析的希望.

研究的目的:

评估SHRED算法监测运行性能的潜力.
训练和测试对象特定的SHRED模型,用于将单个IMU输入映射到多个IMU输出.
调查输入参数 (传感器位置,类型,采样率,速度) 对SHRED推断准确度的影响.

主要方法:

在跑步机上跑步的9名受试者身上训练并测试了特定对象的SHRED模型.
将一个IMU的数据映射到剩余的三个IMU.
多种传感器位置,传感器类型,采样率和运行速度以评估推断错误.

主要成果:

传感器位置和类型没有显著影响SHRED推断准确度.
采样率下降影响了脚测量的准确性.
推断的脚加速仍然低于最小可检测变化值 (12.0 m/s2).
在多个速度训练/测试时,SHRED模型很难准确地推断IMU测量值低于这个值.

结论:

SHRED展示了从有限的IMU数据中重建密集运行动力学和动力学的潜力.
该方法的准确性对采样速率敏感,特别是在脚测量时.
通过使用更少的传感器来实现更丰富的数据集,SHRED可能会增强运动分析.

关键词:

在IMU,IMU是IMU.加速度计的加速度计.机器学习是机器学习.跑步跑步跑步跑步跑步跑步采样率采样率采样率采样率稀疏的传感感觉到的感觉.

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