Scalability of random forest in myoelectric control

<i>Objective.</i>Myoelectric control systems translate electromyographic (EMG) signals into control commands, enabling immersive human-robot interactions in the real world and the Metaverse. The variability of EMG due to various confounding factors leads to significant performance degradation. Such variability can be mitigated by training a highly generalizable but massively parameterized deep neural network, which can be effectively scaled using a vast dataset. We aim to find an alternative simple, explainable, efficient and parallelizable model, which can flexibly scale up with a larger dataset and scale down to reduce model size, and thereby will significantly facilitate the practical implementation of myoelectric control.<i>Approach.</i>In this work, we discuss the scalability of a random forest (RF) for myoelectric control. We show how to scale an RF up and down during the process of pre-training, fine-tuning, and automatic self-calibration. The effects of diverse factors such as bootstrapping, decision tree editing (pre-training, pruning, grafting, appending), and the size of training data are systematically studied using EMG data from 106 participants including both low- and high-density electrodes.<i>Main results.</i>We examined several factors that affect the size and accuracy of the model. The best solution could reduce the size of RF models by≈500×, with the accuracy reduced by only 1.5%. Importantly, for the first time we report the merit of RF that with more EMG electrodes (higher input dimension), the RF model size would be reduced.<i>Significance.</i>All of these findings contribute to the real time deployment RF models in real world myoelectric control applications.