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Model-Based Control of Individual Finger Movements for Prosthetic Hand Function.
IEEE Transactions on Neural Systems and Rehabilitation Engineering ( IF 4.9 ) Pub Date : 2020-01-20 , DOI: 10.1109/tnsre.2020.2967901
Dimitra Blana , Antonie J. Van Den Bogert , Wendy M. Murray , Amartya Ganguly , Agamemnon Krasoulis , Kianoush Nazarpour , Edward K. Chadwick

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.

中文翻译:

基于模型的假手功能单个手指运动的控制。

近年来,用于手部区别的假肢装置已经取得了很大的进步,以至目前最先进的假肢手的机械灵巧性已接近自然手。但是,用户的控制选项没有跟上发展步伐,这意味着新设备没有得到充分利用。文献中报道的控制技术的有希望的发展已经在商业和临床上取得了有限的成功。先前我们已经描述了可以用于假体控制的手的生物力学模型。这项研究的目的是根据模型预测的手指运动的运动保真度和模型的计算性能来评估该方法的可行性。我们展示了该模型在复制记录的手和手指运动学方面的性能,并在建模和记录的运动之间发现了0.89的平均相关性;我们证明了仿真的计算性能足够快,可以通过机器人的手在循环中实现实时控制。并且我们描述了该模型用于控制对象抓取的用途。尽管在访问足够的驱动信号方面存在一些限制,但是当使用记录的EMG信号驱动时,模型性能显示出作为假手控制器的希望。在将来的工作中,有必要对被截肢者进行在环测试,以评估可用驾驶信号的适用性,并检查离线结果到在线性能的转换。我们表明,仿真的计算性能足够快,可以通过机器人手在循环中实现实时控制。并且我们描述了该模型用于控制对象抓取的用途。尽管在访问足够的驱动信号方面存在一些限制,但是当使用记录的EMG信号驱动时,模型性能显示出作为假手控制器的希望。在将来的工作中,有必要对被截肢者进行在环测试,以评估可用驾驶信号的适用性,并检查离线结果到在线性能的转换。我们表明,仿真的计算性能足够快,可以通过机器人手在循环中实现实时控制。并且我们描述了该模型用于控制对象抓取的用途。尽管在访问足够的驱动信号方面存在一些限制,但是当使用记录的EMG信号驱动时,模型性能显示出作为假手控制器的希望。在将来的工作中,有必要对被截肢者进行在环测试,以评估可用驾驶信号的适用性,并检查离线结果到在线性能的转换。当以记录的EMG信号驱动时,模型性能显示出作为假手控制器的希望。在将来的工作中,有必要对被截肢者进行在环测试,以评估可用驾驶信号的适用性,并检查离线结果到在线性能的转换。当以记录的EMG信号驱动时,模型性能显示出作为假手控制器的希望。在将来的工作中,有必要对被截肢者进行在环测试,以评估可用驾驶信号的适用性,并检查离线结果到在线性能的转换。
更新日期:2020-03-20
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