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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition
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A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition

机译：基于协同作用的优化设计传感手套用于功能抓取识别

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摘要

Achieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness.
机译：实现准确而可靠的运动学手势重建是一项艰巨的任务。造成这种情况的主要原因是手部生物力学的复杂性，沿连续的可变形结构分布了多个自由度。可穿戴式传感可以解决此问题，是一种可行的解决方案，因为它可以实现更自然的运动学监控。但是，可穿戴手部姿势重建（HPR）系统的固有精度（以及传感元件的数量）可能会因人体工程学和成本因素而受到严重限制。在本文中，我们将基于手协同信息（即关节间协变模式）的HPR设备的最佳设计的理论基础与基于针织压阻织物（KPF）技术的纺织测角仪相结合，进行了首次开发时间，这是一种经过优化设计的感应不足的手套，用于测量手部运动学。我们仅使用五个最佳放置在手上的传感器，并利用协同信息完成了手势重建（根据具有19个自由度的运动学模型进行了描述）。我们从五个不同的主题获得的重构用于实现一种无监督的方法来识别八种功能性抓握，显示出很高的准确性和鲁棒性。

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