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使用计算机视觉系统协助上肢假肢用于材料检测.

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    此摘要是机器生成的。

    使用YOLOv8的计算机视觉精确地识别了假手抓住的物体. 这项技术旨在通过识别像杯子这样的抓住物品来改善假肢控制,为用户增强功能.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 计算机视觉 计算机视觉
    • 机器人技术 机器人技术 机器人技术

    背景情况:

    • 计算机视觉系统在现实世界的假肢控制中与杂的背景作斗争.
    • 肌电假肢需要先进的控制策略来提高功能.

    研究的目的:

    • 评估在肌电假肢手握任务中使用YOLOv8用于对象识别的可行性.
    • 将定制的物体检测模型与基于EMG的假肢控制器集成.

    主要方法:

    • 一个最先进的物体检测模型 (YOLOv8) 被训练来识别各种类型的杯子.
    • 定制的YOLOv8模型与基于EMG的假肢手控制器集成.
    • 一个人类实验对象执行了抓住对象的任务,以评估模型的性能.

    主要成果:

    • 定制的YOLOv8模型在识别抓住的物体时实现了大约93%的分类准确性.
    • 该模型在对象抓取任务中表现出强大的性能.

    结论:

    • 使用YOLOv8进行物体检测对于肌电假肢手掌控是可行的.
    • 未来的工作将利用对象识别来动态调整握力参数,以增强假肢功能.