Cognitive Workload Classification of Upper-limb Prosthetic Devices
Cognitive Workload Classification of Upper-limb Prosthetic Devices
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DOI：
10.1109/ichms56717.2022.9980676
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Junho Park;Joseph Berman;Albert Dodson;Yunmei Liu;Armstrong Matthew;H. Huang;D. Kaber;Jaime Ruiz
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文献类型：
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作者：
Junho Park;Joseph Berman;Albert Dodson;Yunmei Liu;Armstrong Matthew;H. Huang;D. Kaber;Jaime Ruiz
肢体截肢会在日常生活（ADL）的表演活动中引起严重的功能残疾。使用假体设备作为此类活动的辅助工具需要大量的认知资源。机器学习（ML）算法可用于预测设计过程中假体设备原型的认知工作负载（CW），并作为改善设备可用性的工具。这项研究的目的是探索在设计周期的早期阶段很容易捕获的输入特征的子集，以对基于肌电图（EMG）的CW分类。对30名参与者进行了一项实验，以收集任务绩效和羽毛状数据，并为生成认知绩效模型（CPM）结果提供了基础。开发了三种ML算法，包括随机森林（RF），支撑矢量机（SVM）和天真的贝叶斯（NB）分类器。最重要的特征子集是根据分类准确性以及计算和实验成本选择的。调查结果表明，CPM结果和假体设备配置是在低成本下合理对CW响应进行合理分类的最重要功能。此外，SVM分类器可用于CW的近实际时间分类。未来的研究应包括其他数据并改善高参数调谐参数，以及提高算法性能的高级CPM技术。
Limb amputation can cause severe functional disability in performing activities of daily living (ADLs). Using prosthetic devices as aids for such activities requires substantial cognitive resources. Machine Learning (ML) algorithms can be used to predict cognitive workload (CW) of prosthetic device prototypes early in the design process and serve as a tool for improving device usability. The objective of this study was to explore subsets of input features that can be easily captured during early stages of the design cycle to classify CW of electromyography (EMG)-based upper-limb prostheses. An experiment was conducted with 30 participants to collect task performance and pupillometry data, and to provide a basis for generating cognitive performance model (CPM) outcomes. Three ML algorithms, including the random forest (RF), support vector machine (SVM), and naive Bayesian (NB) classifier were developed. The most important subset of features was selected based on classification accuracy and computational and experimental cost. Findings revealed that the CPM outcomes and prosthetic device configuration were the most important features for reasonably classifying CW responses under low cost. Also, the SVM classifier can be used for near-real time classification of CW. Future studies should include additional data and improve hyperparameter tuning parameters, as well as advanced CPM techniques to improve the performance of algorithms.