A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition.
A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition.
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一种用于长期肌电图 (EMG) 模式识别的新型无监督自适应学习方法
DOI：
10.3390/s17061370
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发表时间：
2017-06-13
期刊：
影响因子：
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通讯作者：
Kotani K
中科院分区：
文献类型：
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作者：
Huang Q;Yang D;Jiang L;Zhang H;Liu H;Kotani K
长期来看，基于模式识别的肌电控制方法会受到各种干扰因素的影响，导致性能下降。提出了一种低计算代价的自适应学习方法，以缓解无监督自适应学习场景的影响。通过构造粒子自适应学习策略和通用增量最小二乘支持向量分类器，提出了一种粒子自适应分类器(PAC)。在无监督和有监督的自适应学习场景中，我们比较了PAC、增量支持向量分类器(ISVC)和非自适应SVC(NSVC)在长期模式识别任务中的性能。通过在模拟和真实的长期肌电数据上验证分类性能，比较了再训练时间、代价和识别精度。对真实长期肌电数据的分类结果表明，与非监督自适应学习场景(9.03%±2.23%，p<；0.05)和非监督自适应学习场景(13.38%±2.62%，p=0.001)相比，PAC显著降低了性能退化，并降低了再训练时间开销(每个更新周期2ms，而不是每个更新周期50ms)。
Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).