Liu Jianwei, Sheng Xinjun, Zhang Dingguo, Zhu Xiangyang
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:14-7. doi: 10.1109/EMBC.2014.6943517.
Pattern recognition based myoelectric prostheses (MP) need a training procedure for calibrating the classifier. Due to the non-stationarity inhered in surface electromyography (sEMG) signals, the system should be retrained day by day in long-term use of MP. To boost the training procedure in later periods, we propose a method, namely Mixed-LDA, which computes the parameters of LDA through combining the model estimated on the incoming training samples of the current day with the prior models available from earlier days. An experiment ranged for 10 days on 5 subjects was carried out to simulate the long-term use of MP. Results show that the Mixed-LDA is significantly better than the baseline method (LDA) when few samples are used as training set in the new (current) day. For instance, in the task including 13 hand and wrist motions, the average classification rate of the Mixed-LDA is 88.74% when the number of training samples is 104 (LDA: 79.32%). This implies that the approach has the potential to improve the usability of MP based on pattern recognition by reducing the training time.
基于模式识别的肌电假肢（MP）需要一个用于校准分类器的训练程序。由于表面肌电（sEMG）信号存在非平稳性，在MP的长期使用中，系统需要每天重新训练。为了在后期加快训练过程，我们提出了一种名为混合线性判别分析（Mixed-LDA）的方法，该方法通过将基于当天传入训练样本估计的模型与早期可用的先验模型相结合来计算线性判别分析（LDA）的参数。我们对5名受试者进行了为期10天的实验，以模拟MP的长期使用。结果表明，当在新的（当前的）一天中使用少量样本作为训练集时，混合线性判别分析（Mixed-LDA）明显优于基线方法（LDA）。例如，在包括13种手部和腕部运动的任务中，当训练样本数量为104时，混合线性判别分析（Mixed-LDA）的平均分类率为88.74%（LDA：79.32%）。这意味着该方法有潜力通过减少训练时间来提高基于模式识别的MP的可用性。
