2.793

                    2018影響因子

                    (CJCR)

                    • 中文核心
                    • EI
                    • 中國科技核心
                    • Scopus
                    • CSCD
                    • 英國科學文摘

                    留言板

                    尊敬的讀者、作者、審稿人, 關于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復。謝謝您的支持!

                    姓名
                    郵箱
                    手機號碼
                    標題
                    留言內容
                    驗證碼

                    基于Myo旋轉偏移估計與自適應校正的手勢識別方法

                    李自由 王豐焱 趙新剛 丁其川 張道輝 韓建達

                    李自由, 王豐焱, 趙新剛, 丁其川, 張道輝, 韓建達. 基于Myo旋轉偏移估計與自適應校正的手勢識別方法. 自動化學報, 2020, 46(9): 1896?1907 doi: 10.16383/j.aas.c190028
                    引用本文: 李自由, 王豐焱, 趙新剛, 丁其川, 張道輝, 韓建達. 基于Myo旋轉偏移估計與自適應校正的手勢識別方法. 自動化學報, 2020, 46(9): 1896?1907 doi: 10.16383/j.aas.c190028
                    Li Zi-You, Wang Feng-Yan, Zhao Xin-Gang, Ding Qi-Chuan, Zhang Dao-Hui, Han Jian-Da. The method for gestures recognition based on Myo rotation shifts estimation and adaptive correction. Acta Automatica Sinica, 2020, 46(9): 1896?1907 doi: 10.16383/j.aas.c190028
                    Citation: Li Zi-You, Wang Feng-Yan, Zhao Xin-Gang, Ding Qi-Chuan, Zhang Dao-Hui, Han Jian-Da. The method for gestures recognition based on Myo rotation shifts estimation and adaptive correction. Acta Automatica Sinica, 2020, 46(9): 1896?1907 doi: 10.16383/j.aas.c190028

                    基于Myo旋轉偏移估計與自適應校正的手勢識別方法

                    doi: 10.16383/j.aas.c190028
                    基金項目: 國家自然科學基金(61573340, 61773369, U1813214)資助
                    詳細信息
                      作者簡介:

                      李自由:中國科學院沈陽自動化研究所博士研究生. 主要研究方向為生物電信號處理, 模式識別與機器學習. E-mail: liziyou@sia.cn

                      王豐焱:中國科學院沈陽自動化研究所碩士研究生. 主要研究方向為智能假肢, 機器學習. E-mail: wangfengyan@sia.cn

                      趙新剛:中國科學院沈陽自動化研究所研究員. 2008年獲得中國科學院沈陽自動化研究所博士學位. 主要研究方向為機器人控制, 智能系統與康復機器人. 本文通信作者. E-mail: zhaoxingang@sia.cn

                      丁其川:東北大學機器人科學與工程學院副教授. 2014年獲得中國科學院大學博士學位. 主要研究方向為生物電信號處理, 模式識別, 可穿戴機器人技術. E-mail: dingqichuan@mail.neu.edu.cn

                      張道輝:中國科學院沈陽自動化研究所副研究員. 2018年獲得中國科學院大學博士學位. 主要研究方向為機器人控制技術. E-mail: peiying.chen@ia.ac.cn

                      韓建達:南開大學人工智能學院教授, 中國科學院沈陽自動化研究所研究員. 1998年獲得哈爾濱工業大學博士學位. 主要研究方向為可穿戴機器人, 智能系統, 移動機器人自主控制. E-mail: hanjianda@nankai.edu.cn

                    The Method for Gestures Recognition Based on Myo Rotation Shifts Estimation and Adaptive Correction

                    Funds: Supported by National Natural Science Foundation of China (61573340, 61773369, U1813214)
                    • 摘要: 在基于表面肌電信號(Surface electromyography, sEMG)的手勢識別系統中, 針對Myo環形電極多次實驗間旋轉位置不同導致的識別精度降低問題, 提出了一種基于極坐標系的電極位置偏移估計與自適應校正的識別方法. 該方法首先建立相對于環形肌電傳感器的極坐標系, 提出了極坐標系下活躍極角(Activation polar angle, APA), 用于估計實驗中傳感器相對于初始位置的橫向旋轉偏移角度; 進而建立基于偏移角度的線性變換模型, 在肌電信號特征空間內, 對電極偏移位置下的樣本進行自適應校正. 在8 種常用手勢識別應用中, 設計了兩種實驗范式: 利用傳感器各通道數據循環平移模擬電極橫向旋轉偏移實驗和肌電傳感器在小臂肌肉上的真實旋轉偏移實驗. 結果均表明所提出方法的識別精度遠高于未進行校正的模型識別精度. 因此, 所提出的電極偏移估計與自適應校正識別方法, 不僅有效提高了表面肌電交互系統識別的魯棒性, 也降低了使用者在多次使用時訓練成本與學習負擔.
                    • 圖  1  根據官方指導說明, 將Myo臂環固定在小臂處; 初始位置: 帶有Logo指示燈的通道與中指對齊

                      Fig.  1  The Myo around the lower arm according to official guides. The position where the logo channel and the middle finger are on one line as the initial position

                      圖  2  實驗中8種常用手部動作: 休息、握拳、展掌、剪刀手、捏中指、捏食指、內翻、外翻

                      Fig.  2  Eight common gestures in the experiment: rest, grasp, open hand, peace sign, pinch thumb finger, pinch middle finger, carpal varus, carpal valgus

                      圖  3  基于滑動窗的特征提取, 其中窗長為LW, 滑動為LI

                      Fig.  3  Feature extraction by a sliding window technique with incremental length (LI) and window length (LW)

                      圖  4  電極循環單位為1時, 由模擬法實驗范式產生的電極偏移下表面肌電信號

                      Fig.  4  Electrode-shift sEMG by a simulation paradigm with a circulation step$\tau= 1 $

                      圖  5  基于極坐標系的電極偏移估計與自適應校正的表面肌電手勢識別方法與傳統識別框架的對比.

                      所采集帶標簽的數據集$ X_L $, 分割成訓練集$X_L^{\rm{train}}$ 與測試集$X_L^{\rm{test}}$; 電極偏移干擾新樣本集$ X_N $, 按照原分類器得到的預測結果$ \hat{y}_N $; 或將新樣本集$ X_N $訓練獨立的分類器, 得到訓練集與測試集結果$y_N^{\rm{train}}$ 和$y_N^{\rm{test} }$ . 點劃線表示路徑, 首先估計電極偏移前后的偏移角度$ \delta\alpha $, 以此建立兩者之間的關系$ T_{n\times n} $, 最終經原分類器得到預測結果$ y_{p\dot N} $

                      Fig.  5  The proposed electrode shifts estimation and adaptive correction method for sEMG-based gesture recognition and its traditional comparisons sEMG-based frameworks.

                      Here, the labeled datasets $ X_L $ were split into the training part ($X_L^{\rm{train}}$, $y_L^{\rm{train}}$) and the testing part ($X_L^{\rm{test}}$, $y_L^{\rm{test}}$). The dataset $ X_N $ was interfered by electrode shifts, whose classification results were $ \hat{y}_N $ by the pre-trained classifier. Or, $ X_N $ was used to train an individual classifier, resulting in $y_N^{\rm{train}}$ and $y_N^{\rm{test}}$. As the dot-dash line, the shift $ \Delta \alpha $ was first estimated, and then $ T_{n\times n} $ was established. Finally, $ y_{p\dot N} $ was the estimated results of the interfered by the pre-trained classifier

                      圖  6  橫截面圖和散點圖(a) 截面視角, 包括Myo臂環和小臂各肌肉與骨骼的截面分布; (b) 散點圖: 不同通道上的樣本點繪制在不同極角的極軸上, 點劃線為所提出并定義的活躍極角

                      Fig.  6  The cross-section diagram and scatter diagram (a) the cross-section view of proposed polar coordinate with Myo armband and the lower arm's muscles and bones; (b) the scatter plot with every channel samples on different polar angle axis, and the proposed APA with dot-dash line

                      圖  7  偏移范圍$ \Delta \alpha $$ [{0^\circ}, {45^\circ }) $時, 所有通道電極相對于初始位置偏移情況

                      Fig.  7  Every electrode shifts $ \Delta \alpha $ in a counter-clockwise direction within the range of $ [{0^\circ}, {45^\circ }) $

                      圖  8  各電極通道MAV特征值映射變換與偏移角度間關系

                      Fig.  8  The mapping relationship between original one-channel MAV and shifted MAV on the shift angle

                      圖  9  模擬電極偏移情況下, 極坐標內的肌電信號樣本點的散點圖與虛線所代表的活躍極角

                      Fig.  9  Scatter plots in the polar coordinate of sEMG samples from simulated electrode shifts and the proposed activation polar angle (APA) in dash line

                      圖  10  不同偏移程度下的手勢識別精度對比: 偏移估計與自適應校正的精度(實線)和未處理電極偏移干擾下的精度(虛線)

                      Fig.  10  Gestures recognition comparison with different shifts: proposed shifts estimation and adaptively corrected accuracies (solid line) v.s. un-corrected accuracies (dotted line)

                      圖  11  真實的電極?肌肉偏移情況下, 極坐標系內肌電信號樣本點的散點圖與虛線所代表的活躍極角

                      Fig.  11  Scatter plots in the polar coordinate of sEMG samples from real electrode shifts and the proposed activation polar angle (APA) in the dash line

                      圖  12  在9次電極?肌肉偏移過程中, 對活躍極角的偏移角度估計

                      Fig.  12  The estimations of activation polar angle for nine shifts in $ [0^{\circ},\, 360^{\circ}) $

                      圖  13  不同真實電極?肌肉偏移程度下的手勢識別精度對比: 偏移估計與自適應校正的精度(實線)和未處理電極偏移干擾下的精度(虛線)

                      Fig.  13  Gesture recognition accuracy comparison with different electrode-muscle shifts: proposed shifts estimation and adaptively corrected accuracies (solid line) v.s. un-corrected accuracies (dotted line)

                      表  1  基于通道循環平移下的模擬電極偏移實驗, 活躍極角估計, 與相對于初始位置旋轉角度估計(°)

                      Table  1  The simulation experiments by circulations of each channel, the estimation of proposed activation polar angle – APA, and the estimation of shifts angle relative to the initial position (°)

                      偏轉實驗$\tau$ 活躍極角$\alpha$ 偏轉角度$\Delta\alpha$
                      0 –173.15
                      1 141.85 45
                      2 96.85 90
                      3 51.85 135
                      4 6.85 180
                      5 –38.15 225
                      6 –83.15 270
                      7 –128.15 315
                      8 –173.15 360
                      下載: 導出CSV

                      表  2  基于電極旋轉的真實電極偏移實驗, 活躍極角估計, 與相對于初始位置旋轉角度估計(°)

                      Table  2  The real experiments by electrode rotation, the estimation of proposed activation polar angle – APA, and the estimation of shifts angle relative to the initial position (°)

                      偏轉實驗$\tau$ 活躍極角$\alpha$ 偏轉角度$\Delta\alpha$
                      0 –169.58 –3.57
                      1 150.09 36.76
                      2 104.74 82.11
                      3 64.97 121.88
                      4 6.29 180.57
                      5 ?39.97 226.83
                      6 –83.01 269.87
                      7 –122.34 309.20
                      8 –175.31 2.17
                      下載: 導出CSV

                      表  3  每名實驗參與者在不同位置或不同重復次數下的交叉驗證平均識別結果($ \star $表示為女性受試者) (%)

                      Table  3  Cross-validation based average accuracies for every subject in different shifted positions or repeated trials ($ \star $ indicates women subjects) (%)

                      被試者 校正前精度 校正后精度
                      1 38.21$\pm$29.88 86.56$\pm$12.04
                      2 29.98$\pm$25.65 74.16$\pm$26.81
                      3 24.54$\pm$28.48 90.06$\pm$8.16
                      4 30.11$\pm$26.59 87.27$\pm$9.51
                      5$\star$ 36.95$\pm$29.05 75.27$\pm$14.20
                      6 31.84$\pm$33.57 85.19$\pm$9.50
                      7 25.91$\pm$31.66 78.24$\pm$13.61
                      8 22.62$\pm$29.25 82.26$\pm$11.81
                      9 26.72$\pm$25.09 64.16$\pm$13.50
                      10 25.27$\pm$26.12 73.92$\pm$14.72
                      11 21.96$\pm$24.52 61.84$\pm$16.01
                      12$\star$ 25.00$\pm$28.86 81.53$\pm$13.72
                      均值 28.26 78.37 ($p<0.001$)
                      下載: 導出CSV
                      360彩票
                    • [1] 丁其川, 熊安斌, 趙新剛, 韓建達. 基于表面肌電的運動意圖識別方法研究及應用綜述. 自動化學報, 2016, 42(1): 13?25

                      Ding Qi-Chuan, Xiong An-Bin, Zhao Xin-Gang, Han JianDa. A review on researches and applications of sEMG-based motion intent recognition methods. Acta Automatica Sinica, 2016, 42(1): 13?25
                      [2] 丁其川, 趙新剛, 韓建達. 基于肌電信號的上肢多關節連續運動估計. 機器人, 36(4): 469−476

                      Ding Qi-Chuan, Zhao Xin-Gang, Han Jian-Da. EMG-based estimation for multi-joint continuous movement of human upper limb. Robot, 2014, 36(4): 469−476
                      [3] 侯增廣, 趙新剛, 程龍, 王啟寧, 王衛群. 康復機器人與智能輔助系統的研究進展. 自動化學報, 2016, 42(12): 1765?1779

                      Hou Zeng-Guang, Zhao Xin-Gang, Cheng Long, Wang Qi-Ning, Wang Wei-Qun. Recent advances in rehabilitation robots and intelligent assistance systems. Acta Automatica Sinica, 2016, 42(12): 1765?1779
                      [4] Lu Z Y, Tong K Y, Shin H, Li S, Zhou P. Advanced myoelectric control for robotic hand-assisted training outcome from a stroke patient. Frontiers in Neurology, 2017, 8: 107
                      [5] Otr O V, Reinders-Messelink H A, Bongers R M, Bouwsema H, Van Der Sluis C K. The i-LIMB hand and the DMC plus hand compared a case report. Prosthetics and Orthotics International, 2010, 34(2): 216?220 doi: 10.3109/03093641003767207
                      [6] Chu J U, Moon I, Lee Y J, Kim S K, Mun M S. A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Transactions on Mechatronics, 2007, 12(3): 282?290 doi: 10.1109/TMECH.2007.897262
                      [7] Scheme E J, Englehart K B, Hudgins B S. Selective classification for improved robustness of myoelectric control under nonideal conditions. IEEE Transactions on Bio-medical Engineering, 2011, 58(6): 1698?1705 doi: 10.1109/TBME.2011.2113182
                      [8] 丁其川, 趙新剛, 李自由, 韓建達. 基于自更新混合分類模型的肌電運動識別方法. 自動化學報, 2019, 45(8): 1464?1474 doi: 10.16383/j.aas.2018.c170301

                      Ding Qi-Chuan, Zhao Xin-Gang, Li Zi-You, Han Jian-Da. The EMG-motion recognition method based on self-update hybrid classification model. Acta Automatica Sinica, 2019, 45(8): 1464?1474 doi: 10.16383/j.aas.2018.c170301
                      [9] Tkach D, Huang H, Kuiken T A. Study of stability of time-domain features for electromyographic pattern recognition. Journal of NeuroEngineering and Rehabilitation, 2010, 7(1): 21 doi: 10.1186/1743-0003-7-21
                      [10] Scheme E, Englehart K. Training strategies for mitigating the effect of proportional controlon classification in pattern recognition based myoelectriccontrol. Journal of Prosthetics and Orthotics, 2013, 25(25): 76?83
                      [11] He J Y, Sheng X J, Zhu X Y, Jiang N. Electrode density affects the robustness of myoelectric pattern recognition system with and without electrode shift. IEEE Journal of Biomedical and Health Informatics, 2019, 23(1): 156?163
                      [12] Kristin ?stlie, Lesj? I M, Franklin R J, Garfelt B, Magnus P. Prosthesis rejection in acquired major upper-limb amputees a population-based survey. Disability and Rehabilitation. Assistive technology, 2011, 7(4): 294?303
                      [13] Biddiss E A, Chau T T. Upper limb prosthesis use and abandonment a survey of the last 25 years. Prosthetics and Orthotics International, 2007, 31(3): 236?257 doi: 10.1080/03093640600994581
                      [14] Stango A, Negro F, Farina D. Spatial correlation of high density emg signals provides features robust to electrode number and shift in pattern recognition for myocontrol. IEEE Transactions on Neural Systems Rehabilitation Engineering, 2015, 23(2): 189?198 doi: 10.1109/TNSRE.2014.2366752
                      [15] Mohanaiah P, Sathyanarayana P, GuruKumar L. Image texture feature extraction using GLCM approach. International Journal of Scientific and Research Publications, 2013, 3(5): 1?5
                      [16] He J F, Zhu X Y. Combining improved gray-level co-occurrence matrix with high density grid for myoelectric control robustness to electrode shift. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(9): 1539?1548 doi: 10.1109/TNSRE.2016.2644264
                      [17] Pan, L Z, Zhang D G, Jiang N, Sheng X J, Zhu, X. Y. Improving robustness against electrode shift of high density emg for myoelectric control through common spatial patterns. Journal of Neuroengineering Rehabilitation, 2015, 12(1): 1?16 doi: 10.1186/1743-0003-12-1
                      [18] Young A J, Hargrove L J, Kuiken T A. Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration. IEEE Transactions on Bio-medical Engineering, 2012, 59(3): 645?652 doi: 10.1109/TBME.2011.2177662
                      [19] Rawat S, Vats S, Kumar P. Evaluating and exploring the MYO ARMBAND. In: Proceedings of the 2016 International Conference System Modeling and Advancement in Research Trends (SMART), Moradabad, India, 2016. 115?120
                      [20] Getting starting with Myo on Windows. Available from: https://support.getmyo.com
                      [21] 王松. 運動解剖學. 武漢, 華中科技大學出版社, 2018, 113?117

                      Wang Song. Sport Anatomy. Wuhan, Huazhong University of Science and Technology Press, 2018, 113?117
                    • 加載中
                    圖(13) / 表(3)
                    計量
                    • 文章訪問數:  274
                    • HTML全文瀏覽量:  99
                    • PDF下載量:  66
                    • 被引次數: 0
                    出版歷程
                    • 收稿日期:  2019-01-09
                    • 錄用日期:  2019-06-24
                    • 網絡出版日期:  2020-09-28
                    • 刊出日期:  2020-09-28

                    目錄

                      /

                      返回文章
                      返回