Model-based control of individual finger movements for prosthetic hand function
Model-based control of individual finger movements for prosthetic hand function
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基于模型的单个手指运动控制以实现假手功能
DOI：
10.1101/629246
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Blana D
中科院分区：
文献类型：
--
作者：
Blana D
近年来，用于手差异的假体装置已经相当先进，达到了最先进的假手的机械灵活性接近自然手的程度。然而，用户的控制选项没有跟上步伐，这意味着新设备没有充分发挥其潜力。文献中报道的控制技术的有希望的发展已经遇到了有限的商业和临床成功。我们以前描述了一个生物力学模型的手，可用于假肢控制。本研究的目的是评估这种方法的可行性方面的模型预测的手指运动的运动学保真度和模型的计算性能。我们展示了该模型在复制记录的手和手指运动学的性能，并发现建模和记录的运动之间的平均相关性为0.89;我们表明，模拟的计算性能足够快，可以实现实时控制与机器人手在循环中;我们描述了使用该模型控制物体抓取。尽管在获取足够的驱动信号方面存在一些限制，但当使用记录的EMG信号驱动时，模型性能显示出作为假肢手控制器的希望。在未来的工作中，有必要对截肢者进行用户在环测试，以评估可用驱动信号的适用性，并检查离线结果到在线性能的转换。
Prosthetic devices for hand difference have advanced considerably in recent years, to the point where the mechanical dexterity of a state-of-the-art prosthetic hand approaches that of the natural hand. Control options for users, however, have not kept pace, meaning that the new devices are not used to their full potential. Promising developments in control technology reported in the literature have met with limited commercial and clinical success. We have previously described a biomechanical model of the hand that could be used for prosthesis control. The goal of this study was to evaluate the feasibility of this approach in terms of kinematic fidelity of model-predicted finger movement and the computational performance of the model. We show the performance of the model in replicating recorded hand and finger kinematics and find average correlations of 0.89 between modelled and recorded motions; we show that the computational performance of the simulations is fast enough to achieve real-time control with a robotic hand in the loop; and we describe the use of the model for controlling object gripping. Despite some limitations in accessing sufficient driving signals, the model performance shows promise as a controller for prosthetic hands when driven with recorded EMG signals. User-in-the-loop testing with amputees is necessary in future work to evaluate the suitability of available driving signals, and to examine translation of offline results to online performance.
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