Comparison of Machine Learning approaches for Classifying Upper Extremity Tasks in Individuals Post-Stroke.
Comparison of Machine Learning approaches for Classifying Upper Extremity Tasks in Individuals Post-Stroke.
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对中风后个体上肢任务进行分类的机器学习方法的比较。
DOI：
10.1109/embc44109.2020.9176331
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Wade,Eric
中科院分区：
文献类型：
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作者：
Miller,Aaron;Quinn,Lori;Duff,SusanV;Wade,Eric
中风后，个体通常表现出上肢（UE）运动功能障碍，影响日常任务的表现。表征UE移动对于跟踪恢复和对干预的响应是有用的。然而，由于恢复过程的复杂性，UE移动可能是极其可变的并且是个人特定的。虽然这使得这些手势的自动识别具有挑战性，但机器学习方法可用于对非典型人群中的UE移动进行分类。在目前的研究中，我们利用数据从20个人中风后和20个年龄匹配的控制，以确定一组最佳的传感器提取的功能进行分类的单手和双手手势在任务执行。我们发现，使用少于100个特征沿着与随机森林分类器在两组中产生了最好的性能，无论是用户依赖的还是用户独立的模型。
After a stroke, individuals often exhibit upper extremity (UE) motor dysfunction, influencing the performance of everyday tasks. Characterizing UE movements is useful to track recovery and response to intervention. Yet, due to the complexity of the recovery process, UE movements may be extremely variable and person-specific. While this renders automatic recognition of these gestures challenging, machine learning methods could be used to classify UE movements in atypical populations. In the current study, we utilize data from 20 individuals post-stroke and 20 age-matched controls to identify an optimal set of sensor-extracted features for the classification of unimanual and bimanual gestures during task performance. We found that using fewer than 100 features along with a random forest classifier produced the best performance across both groups, with both user-dependent and user-independent models.