本文選題：肌電控制 + 手勢(shì)識(shí)別　； 參考：《中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào)》2016年05期

【摘要】：動(dòng)作肌電信號(hào)具有個(gè)體差異性且不同動(dòng)作的肌電信號(hào)是不同的,通過(guò)挖掘雙線性模型的因素分解能力,將訓(xùn)練樣本的特征矢量分解為用戶相關(guān)和動(dòng)作相關(guān)兩大因素,通過(guò)確定因素的維度重構(gòu)具有共性的訓(xùn)練樣本特征。在測(cè)試樣本特征重構(gòu)階段引入適應(yīng)融合機(jī)制,更新模型參數(shù)重構(gòu)測(cè)試樣本特征。以11名受試者的4類動(dòng)作為例,分別采用線性判別、K近鄰分類算法和支持向量機(jī),對(duì)比3種實(shí)驗(yàn)方案(多用戶單天、單用戶多天和基于雙線性模型的多用戶單天)的識(shí)別結(jié)果。實(shí)驗(yàn)表明,雙線性模型的平均識(shí)別率最低為85%以上,相比于單純的多用戶單天識(shí)別結(jié)果(平均識(shí)別率不高于75%)有顯著提高(P0.001),且相比于單用戶多天的識(shí)別結(jié)果(平均識(shí)別率90%以上)差異性不顯著(P0.24)。雙線性模型為基于動(dòng)作識(shí)別技術(shù)的非特定人肌電控制系統(tǒng)提供了交互方案,且該模型具備將多用戶單天的數(shù)據(jù)看成單用戶多天數(shù)據(jù)的能力,提供了用戶訓(xùn)練負(fù)擔(dān)降低的可行性。
[Abstract]:EMG signals have individual differences and EMG signals of different actions are different. By mining the factor decomposition ability of bilinear model, the feature vectors of training samples are decomposed into two major factors: user correlation and action correlation. By determining the factors of dimension reconstruction has the common characteristics of the training sample. The adaptive fusion mechanism is introduced in the phase of test sample feature reconstruction, and the model parameters are updated to reconstruct the test sample feature. Taking four kinds of actions of 11 subjects as examples, the recognition results of three experimental schemes (multi-user single day, single-user multi-day and bilinear model based multi-user single-day) were compared by using linear discriminant K-nearest neighbor classification algorithm and support vector machine. Experiments show that the average recognition rate of bilinear model is more than 85%. Compared with the simple multi-user recognition results (the average recognition rate is not higher than 7575), the P0.001D is significantly improved, and the difference is not significant compared with the single-user multi-day recognition results (the average recognition rate is more than 90%), and the difference is not significant (P 0.24%) compared with the single-user multi-day recognition results (the average recognition rate is more than 90%). The bilinear model provides an interactive scheme for an independent EMG control system based on motion recognition technology, and the model has the ability to treat multi-user single-day data as single-user multi-day data, and provides the feasibility of reducing the user training burden.
【作者單位】： 合肥工業(yè)大學(xué)儀器科學(xué)與光電工程學(xué)院生物醫(yī)學(xué)工程系;
【基金】：國(guó)家自然科學(xué)基金(61401138;81571760;61501164)
【分類號(hào)】：R318;TP391.41

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