One-Shot Random Forest Model Calibration for Hand Gesture Decoding
One-Shot Random Forest Model Calibration for Hand Gesture Decoding
复制标题
用于手势解码的一次性随机森林模型校准
DOI：
10.1101/2023.07.21.550033
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Jiang X
中科院分区：
文献类型：
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作者：
Jiang X
目的大多数现有的肌电控制机器学习模型都需要大量的数据来学习用户特定的肌电信号特征，这是一项繁重的工作。我们的目标是开发一种方法，使从一个新的肌电用户的数据最少的预训练模型的校准。方法：我们使用20个人在多次握拍过程中收集的肌电图数据训练随机森林（RF）模型。为了适应新用户的决策规则，首先，使用新用户的验证数据对预训练决策树的分支进行修剪。然后，仅使用来自新用户的数据训练的新决策树被附加到修剪后的预训练模型中。结果18名参与者为期两天的实时肌电实验表明，与基准用户特定射频和线性判别分析模型相比，该方法的准确性有所提高。此外，与基准方法相比，在第一天为新参与者校准的RF模型在第二天产生了显着更高的准确性，这反映了所提出方法的鲁棒性。意义提出的模型校准过程是完全无源的，即一旦基本模型被预训练，就不需要访问原始20人的源数据。我们的工作促进了高效、可解释和简单的肌电控制模型的使用。
ObjectiveMost existing machine learning models for myoelectric control require a large amount of data to learn user-specific characteristics of the electromyographic (EMG) signals, which is burdensome. Our objective is to develop an approach to enable the calibration of a pre-trained model with minimal data from a new myoelectric user.ApproachWe trained a random forest (RF) model with EMG data from 20 people collected during the performance of multiple hand grips. To adapt the decision rules for a new user, first, the branches of the pre-trained decision trees were pruned using the validation data from the new user. Then new decision trees trained merely with data from the new user were appended to the pruned pre-trained model.ResultsReal-time myoelectric experiments with 18 participants over two days demonstrated the improved accuracy of the proposed approach when compared to benchmark user-specific RF and the linear discriminant analysis models. Furthermore, the RF model that was calibrated on day one for a new participant yielded significantly higher accuracy on day two, when compared to the benchmark approaches, which reflects the robustness of the proposed approach.SignificanceThe proposed model calibration procedure is completely source-free, that is, once the base model is pre-trained, no access to the source data from the original 20 people is required. Our work promotes the use of efficient, explainable, and simple models for myoelectric control.