IMU Sensors Measurements Towards the Development of Novel Prosthetic Arm Control Strategies.

Riccardo Galviati, Nicolo Boccardo, Michele Canepa, Dario Di Domenico, Andrea Marinelli, Carlo Albino Frigo, Matteo Laffranchi, Lorenzo de Michieli
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Abstract

The complexity of the human upper limb makes replicating it in a prosthetic device a significant challenge. With advancements in mechatronic developments involving the addition of a large number of degrees of freedom, novel control strategies are required. To accommodate this need, this study aims at developing an IMU-based control for the HannesARM upper-limb prosthetic device, as a proof-of-concept for new control strategies integrating data-fusion approaches. The natural human control of the upper-limb is based on different inputs that allow adaptive control. To mimic this in prostheses, the implementation of IMUs provides kinematic information of both the stump and the prosthesis to enrich the EMG control. The principle of operation is to decode upper limb movements by using a custom-made system and to replicate them in prosthetic arms improving the control algorithms. To evaluate the system's effectiveness, the custom algorithm's motion extraction was compared to a motion capture system using fifteen able-bodied subjects. The results showed that this system scored 0.16 ± 0.04 and 0.81 ± 0.12 in Root Mean Squared Error and Cross-Correlation compared to the motion capture system. Experimental results demonstrate how this work can extract valuable kinematic information necessary for new and improved control strategies, such as intention detection or pattern recognition, to allow users to perform a broader range of tasks and enhancing in turn their quality of life.

IMU传感器测量促进新型假肢控制策略的发展。
人类上肢的复杂性使得在假肢装置中复制它成为一项重大挑战。随着机电一体化发展的进步,包括增加了大量的自由度,需要新的控制策略。为了满足这一需求,本研究旨在为HannesARM上肢假肢设备开发一种基于IMU的控制,作为集成数据融合方法的新控制策略的概念证明。人类对上肢的自然控制基于允许自适应控制的不同输入。为了在假体中模拟这一点,IMU的实现提供了残端和假体的运动学信息,以丰富EMG控制。操作原理是通过使用定制系统解码上肢运动,并在假肢中复制它们,从而改进控制算法。为了评估该系统的有效性,将自定义算法的运动提取与使用15名健全受试者的运动捕捉系统进行了比较。结果表明,与运动捕捉系统相比,该系统在均方根误差和互相关方面的得分分别为0.16±0.04和0.81±0.12。实验结果表明,这项工作可以提取新的和改进的控制策略所需的有价值的运动学信息,如意图检测或模式识别,以允许用户执行更广泛的任务,进而提高他们的生活质量。
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