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实时手部康复:FPGA加速神经网络用于肌肉活动分类.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 计算机工程 计算机工程
    • 人工智能的人工智能

    背景情况:

    • 从肌肉活动中检测手部运动意图对于辅助和康复设备至关重要.
    • 当前基于微控制器的系统可能缺乏无人机交互所需的实时响应能力.

    研究的目的:

    • 开发和评估基于FPGA的神经网络,用于分类手部运动意图的肌肉活动.
    • 为了证明硬件加速对于实时生物医学信号处理的可行性.

    主要方法:

    • 在FPGA上使用高层合成 (HLS) 设计并实施了三层神经网络.
    • 该系统实时处理肌肉传感器数据,将肌肉状态分类为压力 (运动意图) 或放松 (无运动意图).
    • 该FPGA系统集成到一个可穿戴的手部运动辅助原型中.

    主要成果:

    • 该FPGA实现实现了低延迟和节能运行,优于传统的微控制器方法.
    • 硬件加速的神经网络以紧的设计展示了肌肉活动的高效分类.
    • 该系统成功地根据分类的肌肉状态在原型中触发了受控的手动.

    结论:

    • 基于FPGA的神经网络为实时生物医学信号处理提供了一个有希望的,快速和高效的替代方案.
    • 开发的系统显示了增强辅助和康复应用的巨大潜力.
    • 未来的工作重点是优化硬件资源,扩大对假肢和更广泛的康复场景的应用.