从动力学预测静态和移动达到目标的深度和方向效应
A Bosco1,2, M Filippini3,4, D Borra5
1Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy. annalisa.bosco2@unibo.it.
Scientific reports
|August 12, 2023
概括
在reach-to-grasp运动中的行动意图适应不断变化的视觉目标目标. 研究人员使用循环神经网络来解码运动动力学,揭示了对目标转移的时间预测结构.
科学领域:
- 神经科学是一个神经科学.
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
背景情况:
- 从伸到抓的运动动力学受到行动意图的影响.
- 意想不到的视觉目标对行动意图和运动期间时间预测的影响仍然不清楚.
研究的目的:
- 调查行动意图是否会在达成过程中随着目标目标的修改而改变.
- 为了确定目标目标预测的时间结构,在达到-抓取运动期间.
- 分析动态数据来解码运动意图和预测目标转移.
主要方法:
- 在达到运动时记录了23名参与者的运动数据 (手指和手腕).
- 在各种位置引入静态和移动的视觉目标.
- 采用循环神经网络来从动态数据中对最终目标和中间轨迹进行时间解码.
主要成果:
- 从运动开始到完成,分类性能逐渐提高.
- 成功解码了静态和移动目标.
- 横向目标的解码准确度超过了斜向目标的解码准确度.
结论:
- 人类的触摸-抓取动作表现出适应性的动力学策略,以应对不断变化的视觉目标.
- 运动动力学的时间解码可以揭示目标转移的预测机制.
- 通过动力学分析,研究结果为优化3D空间中人机器人协作提供了信息.
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