Robotic hand prostheses require a controller to decode muscle contraction information, such as electromyogram (EMG) signals, into the user’s desired hand movement. State-of-the-art decoders demand extensive training, require data from a large number of EMG sensors and are prone to poor predictions. Biomechanical models of a single movement degree-of-freedom tell us that relatively few muscles, and, hence, fewer EMG sensors are needed to predict movement. We propose a novel decoder based on a dynamic, functional linear model with velocity or acceleration as its response and the recent past EMG signals as functional covariates. The effect of each EMG signal varies with the recent position to account for biomechanical features of hand movement, increasing the predictive capability of a single EMG signal compared to existing decoders. The effects are estimated with a multistage, adaptive estimation procedure that we call Sequential Adaptive Functional Estimation (SAFE). Starting with 16 potential EMG sensors, our method correctly identifies the few EMG signals that are known to be important for an able-bodied subject. Furthermore, the estimated effects are interpretable and can significantly improve understanding and development of robotic hand prostheses.

中文翻译：

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具有顺序自适应功能估计（SAFE）的机器人假体控制器的最佳EMG放置

机械手假肢需要控制器将肌肉收缩信息（例如肌电图（EMG）信号）解码为用户所需的手部运动。最先进的解码器需要大量的培训，需要来自大量EMG传感器的数据，并且容易产生不良的预测。单个运动自由度的生物力学模型告诉我们，相对较少的肌肉，因此，需要较少的EMG传感器来预测运动。我们提出了一种基于动态，功能线性模型的新型解码器，该模型以速度或加速度为响应，最近的过去EMG信号为功能协变量。每个EMG信号的影响随最近位置而变化，以说明手部运动的生物力学特征，与现有解码器相比，增加了单个EMG信号的预测能力。通过多阶段的自适应估计程序来估计效果，我们将其称为顺序自适应功能估计（SAFE）。从16个潜在的EMG传感器开始，我们的方法可以正确识别一些已知对身体健康的受试者很重要的EMG信号。此外，估计的效果是可以解释的，并且可以显着提高机器人手部假体的理解和发展。