本文選題：手勢識別　切入點(diǎn)：表面肌電信號(s　出處：《計(jì)算機(jī)工程與應(yīng)用》2017年17期

【摘要】：研究利用三類傳感器(表面肌電儀、陀螺儀和加速度計(jì))信號的特點(diǎn)進(jìn)行信息融合,提高可識別動(dòng)態(tài)手勢動(dòng)作的種類和準(zhǔn)確率。將動(dòng)態(tài)手勢動(dòng)作分解為手形、手勢朝向和運(yùn)動(dòng)軌跡三個(gè)要素,分別使用表面肌電信號(s EMG)、陀螺儀信號(GYRO)和加速度信號(ACC)進(jìn)行表征,利用多流HMMs進(jìn)行動(dòng)態(tài)手勢動(dòng)作的模式識別。對包含有5個(gè)運(yùn)動(dòng)軌跡和6個(gè)靜態(tài)手形的識別實(shí)驗(yàn)結(jié)果表明,該方法可以有效地從連續(xù)信號中識別動(dòng)態(tài)手勢,三類傳感器組合使用獲得的全局平均識別率達(dá)到92%以上,明顯高于任意兩個(gè)傳感器組合和僅采用單個(gè)傳感器獲得的平均識別率。實(shí)驗(yàn)表明該方法是一種有效的動(dòng)態(tài)手勢識別方法,并且相較于傳統(tǒng)的動(dòng)態(tài)手勢識別的方法更具有優(yōu)勢。
[Abstract]:The information fusion of three kinds of sensors (surface electromyograph, gyroscope and accelerometer) is studied in order to improve the classification and accuracy of recognizable dynamic gesture. The three elements of gesture orientation and motion trajectory were characterized by surface electromyography (EMG), gyroscope (gyroscope) and acceleration signal (ACCS), respectively. Multi-stream HMMs is used for dynamic gesture recognition. The experimental results show that the method can effectively recognize dynamic gestures from continuous signals, and the experimental results show that there are five motion trajectories and six static hand shapes. The global average recognition rate obtained by the combination of three kinds of sensors is over 92%. It is obviously higher than the average recognition rate obtained by any combination of two sensors and only using a single sensor. Experiments show that this method is an effective dynamic gesture recognition method and has more advantages than the traditional dynamic gesture recognition method.
【作者單位】： 常州大學(xué)研究生部;常州大學(xué)信息科學(xué)與工程學(xué)院數(shù)理學(xué)院;常州大學(xué)城市軌道交通學(xué)院;
【基金】：國家自然科學(xué)基金(No.61201096) 機(jī)器人技術(shù)與系統(tǒng)國家重點(diǎn)實(shí)驗(yàn)室開放基金重點(diǎn)資助項(xiàng)目(No.SKLRS-2010-2D-09,No.SKLRS-2010-MS-10) 江蘇省高校自然科學(xué)研究面上資助項(xiàng)目(No.10KJB510003) 江蘇省自然科學(xué)基金(No.BK20140265) 常州市科技資助項(xiàng)目(No.CJ20110023,No.CM20123006)
【分類號】：TP212;TP391.41

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