Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors.
Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors.
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DOI：
10.1109/tnsre.2010.2079334
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发表时间：
2011-10
期刊：
影响因子：
--
通讯作者：
Kamper DG
中科院分区：
文献类型：
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作者：
Lee SW;Wilson KM;Lock BA;Kamper DG
在这项研究中，我们开发了一个强大的主题特定的肌电图（EMG）模式分类技术，区分中风幸存者的肌肉激活模式的预期手动任务。这些分类将使人们能够对辅助设备进行意志控制，从而改善其功能。20名慢性轻偏瘫受试者参加了这项研究。受试者被指示执行六项功能任务，同时通过放置在受损肢体前臂和手上的十个表面电极记录他们的肌肉激活模式。为了识别预期的功能任务，使用线性判别分析的模式分类器被施加到EMG特征向量。分类精度主要受试者的损伤程度影响。中度受损受试者的平均分类准确度为71.3%（手4和5的Chedoke分期），重度受损受试者为37.9%（手2和3的Chedoke分期）。大多数错误分类发生在性质相似的抓握任务之间，例如，捏，键和三指抓握之间，或圆柱形和球形抓握之间。肌电信号从内在的手的肌肉显着贡献的任务间变异的特征向量，由任务间的平方欧几里德距离评估，从而表明内在的手的肌肉在功能性手动任务的重要性。这项研究证明了EMG模式分类技术识别中风幸存者意图的可行性。未来的工作应该集中在建设一个特定的主题肌电图分类范式，仔细考虑每个主题的功能和生理障碍的特点，在目标任务的选择和电极放置程序。
In this study, we developed a robust subject-specific electromyography (EMG) pattern classification technique to discriminate intended manual tasks from muscle activation patterns of stroke survivors. These classifications will enable volitional control of assistive devices, thereby improving their functionality. Twenty subjects with chronic hemiparesis participated in the study. Subjects were instructed to perform six functional tasks while their muscle activation patterns were recorded by ten surface electrodes placed on the forearm and hand of the impaired limb. In order to identify intended functional tasks, a pattern classifier using linear discriminant analysis was applied to the EMG feature vectors. The classification accuracy was mainly affected by the impairment level of the subject. Mean classification accuracy was 71.3% for moderately impaired subjects (Chedoke Stage of Hand 4 and 5), and 37.9% for severely impaired subjects (Chedoke Stage of Hand 2 and 3). Most misclassification occurred between grip tasks of similar nature, for example, among pinch, key, and three-fingered grips, or between cylindrical and spherical grips. EMG signals from the intrinsic hand muscles significantly contributed to the inter-task variability of the feature vectors, as assessed by the inter-task squared Euclidean distance, thereby indicating the importance of intrinsic hand muscles in functional manual tasks. This study demonstrated the feasibility of the EMG pattern classification technique to discern the intent of stroke survivors. Future work should concentrate on the construction of a subject-specific EMG classification paradigm that carefully considers both functional and physiological impairment characteristics of each subject in the target task selection and electrode placement procedures.