Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation
Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation
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DOI：
10.3389/fnbot.2016.00019
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发表时间：
2016-11-29
影响因子：
3.1
通讯作者：
Hirata, Hitoshi
中科院分区：
文献类型：
--
作者：
Oyama, Shintaro;Shimoda, Shingo;Hirata, Hitoshi
背景资料：为了机械地重建人体生物力学功能，需要直观的比例控制和对意外情况的鲁棒性。特别是，创造一个功能性的手假体是一个典型的挑战，在重建失去的生物力学功能。然而，目前可用的控制算法处于开发阶段。用于控制多功能假肢的最先进的算法是机器学习和肌电信号的模式识别。尽管这些方法在计算速度上有所提高，但无法避免用户意识的要求和分类分离误差。默会学习系统是一种简单而新颖的自适应控制策略，它能够根据环境的变化自适应地调整姿态。我们介绍了在假体旋转控制的战略，以实现补偿性减少，以及评估系统及其对user.Methods的影响：我们进行了一项非随机研究，涉及8个假体用户进行酒吧搬迁任务与/无默会学习系统的支持。手持件和身体运动被连续记录与测角器，视频，和motion-capture system.Findings：减少在参与者的上肢旋转补偿运动监测在所有参与者的搬迁任务。六分之五的参与者的全身能量消耗的估计概况得到改善。解释：我们的系统迅速完成了几乎自然的运动，没有意外的错误。默会学习系统不仅适应人体运动，而且提高了人类快速适应系统的能力，同时系统放大了残肢产生的补偿。该概念可以扩展到各种情况，用于重建可以补偿的损失功能。
Background: For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. Tacit Learning System is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user.Methods: We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system.Findings: Reduction in the participants' upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants.Interpretation: Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated.