IEEE Trans Biomed Circuits Syst. 2022 Apr;16(2):266-274. doi: 10.1109/TBCAS.2022.3161133. Epub 2022 May 19.
A new concept of human-machine interface to control hand prostheses based on displacements of multiple magnets implanted in the limb residual muscles, the myokinetic control interface, has been recently proposed. In previous works, magnets localization has been achieved following an optimization procedure to find an approximate solution to an analytical model. To simplify and speed up the localization problem, here we employ machine learning models, namely linear and radial basis functions artificial neural networks, which can translate measured magnetic information to desired commands for active prosthetic devices. They were developed offline and then implemented on field-programmable gate arrays using customized floating-point operators. We optimized computational precision, execution time, hardware, and energy consumption, as they are essential features in the context of wearable devices. When used to track a single magnet in a mockup of the human forearm, the proposed data-driven strategy achieved a tracking accuracy of 720 μm 95% of the time and latency of 12.07 μs. The proposed system architecture is expected to be more power-efficient compared to previous solutions. The outcomes of this work encourage further research on improving the devised methods to deal with multiple magnets simultaneously.
一种基于植入肢体残余肌肉中的多个磁铁位移来控制手部假肢的新型人机接口概念，即运动控制接口，最近被提出。在以前的工作中，磁铁的定位是通过优化程序来找到解析模型的近似解来实现的。为了简化和加速定位问题，我们在这里使用了机器学习模型，即线性和径向基函数人工神经网络，它们可以将测量的磁信息转换为主动假肢设备所需的命令。它们是离线开发的，然后使用定制的浮点运算符在现场可编程门阵列上实现。我们优化了计算精度、执行时间、硬件和能源消耗，因为这些都是可穿戴设备环境中的关键特性。当用于跟踪人体前臂模型中的单个磁铁时，所提出的数据驱动策略在 95%的时间内实现了 720μm 的跟踪精度，延迟为 12.07μs。与以前的解决方案相比，所提出的系统架构预计将具有更高的能效。这项工作的结果鼓励进一步研究改进设计方法，以同时处理多个磁铁。
