使用低成本惯性测量单元和边缘计算进行惯性轨迹估计
IEEE journal of biomedical and health informatics
|February 25, 2026
概括
本研究介绍了一个边缘计算系统,用于使用惯性测量单位 (IMU) 准确的轨迹估计. 安全,低功耗的系统为康复和导航等应用提供实时运动跟踪.
科学领域:
- 边缘计算 边缘计算
- 传感器融合式传感器
- 机器学习用于机器人技术
背景情况:
- 轨迹估计对于诸如康复评估和室内导航等应用至关重要.
- 惯性测量单位 (IMU) 提供了一种多功能解决方案,用于在各种环境中估计轨迹.
- 现有的方法经常面临传输延迟,隐私和电力消耗等挑战.
研究的目的:
- 开发一个安全,私有和低功耗的边缘计算系统,用于实时轨迹估计.
- 集成先进的神经网络模型,以提高运动跟踪精度.
- 通过在边缘平台上直接处理数据来最大限度地减少传输延迟.
主要方法:
- 使用Res2Net,一个卷积块注意模块和一个时间卷积网络设计了一个新的轨迹估计模型.
- 为了培训和测试,收集了包括步行和手部运动在内的运动数据集.
- 该模型是在一个带有神经处理单元的边缘计算平台上实现的.
主要成果:
- 拟议的模型实现了高精度,平均根平均平方误差为0.364m.
- 推断时间显著减少到0.234s的20s的IMU数据,超过20%比一个可比模型更快.
- 该系统在各种边缘平台上展示了准确的实时轨迹估计能力.
结论:
- 开发的边缘计算系统提供了一个安全,高效和准确的解决方案,用于使用IMU实时轨迹估计.
- 在边缘设备上集成先进的深度学习模型可以提高运动跟踪应用程序的性能.
- 这种方法克服了传统方法的局限性,在个性化康复和自主导航等领域实现了更广泛的采用.
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