دورية أكاديمية
The effect of attentional focusing strategies on EMG-based classification.
| العنوان: | The effect of attentional focusing strategies on EMG-based classification. |
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| المؤلفون: | Ay AN; Department of Mechatronics Engineering, Sakarya University of Applied Sciences, Esentepe Campus, Serdivan, Sakarya, Turkey., Yildiz MZ; Department of Electrical and Electronics Engineering, Sakarya University of Applied Sciences, Esentepe Campus, Serdivan, Sakarya, Turkey. |
| المصدر: | Biomedizinische Technik. Biomedical engineering [Biomed Tech (Berl)] 2020 Oct 19; Vol. 66 (2), pp. 153-158. Date of Electronic Publication: 2020 Oct 19 (Print Publication: 2021). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Walter de Gruyter Publishers Country of Publication: Germany NLM ID: 1262533 Publication Model: Electronic-Print Cited Medium: Internet ISSN: 1862-278X (Electronic) Linking ISSN: 00135585 NLM ISO Abbreviation: Biomed Tech (Berl) Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2006- : Berlin : Walter de Gruyter Publishers Original Publication: Berlin, Schiele & Schön; Stuttgart, Georg Thieme. |
| مواضيع طبية MeSH: | Attention/*physiology , Electromyography/*methods , Muscle, Skeletal/*physiology, Arm ; Humans ; Movement/physiology |
| مستخلص: | Earlier studies showed that external focusing enhances motor performance and reduces muscular activity compare to internal one. However, low activity is not always desired especially in case of Human-Machine Interface applications. This study is based on investigating the effects of attentional focusing preferences on EMG based control systems. For the EMG measurements via biceps brachii muscles, 35 subjects were asked to perform weight-lifting under control, external and internal focus conditions. The difference between external and internal focusing was found to be significant and internal focus enabled higher EMG activity. Besides, six statistical features, namely, RMS, maximum, minimum, mean, standard deviation, and variance were extracted from both time and frequency domains to be used as inputs for Artificial Neural Network classifiers. The results found to be 87.54% for ANN1 and 82.69% for ANN2, respectively. These findings showed that one's focus of attention would be predicted during the performance and unlike the literature, internal focusing could be also useful when it is used as an input for HMI studies. Therefore, attentional focusing might be an important strategy not only for performance improvement to human movement but also for advancing the study of EMG-based control mechanisms. (© 2020 Walter de Gruyter GmbH, Berlin/Boston.) |
| References: | Shea, CH, Wulf, G. Enhancing motor learning through external-focus instructions and feedback. Hum Mov Sci 1999;18:553–71. https://doi.org/10.1016/s0167-9457(99)00031-7. Lohse, KR, Sherwood, DE, Healy, AF. On the advantage of an external focus of attention: a benefit to learning or performance?. Hum Mov Sci 2014;33:120–34. https://doi.org/10.1016/j.humov.2013.07.022. Wulf, G, McNevin, N, Shea, CH. The automaticity of complex motor skill learning as a function of attentional focus. Q J Exp Psychol Sect A Hum Exp Psychol 2001;54:1143–54. https://doi.org/10.1080/713756012. Wulf, G, McConnel, N, Gartner, M, Schwarz, A. Enhancing the learning of sport skills through external-focus feedback. J Mot Behav 2002;34:171–82. https://doi.org/10.1080/00222890209601939. Park, SH, Yi, CW, Shin, JY, Ryu, YU. Effects of external focus of attention on balance: a short review. J Phys Ther Sci 2015;27:3929–31. https://doi.org/10.1589/jpts.27.3929. Vance, J, Wulf, G, Töllner, T, McNevin, N, Mercer, J. EMG activity as a function of the performer’s focus of attention. J Mot Behav 2004;36:450–9. https://doi.org/10.3200/jmbr.36.4.450-459. Zachry, T, Wulf, G, Mercer, J, Bezodis, N. Increased movement accuracy and reduced EMG activity as the result of adopting an external focus of attention. Brain Res Bull 2005;67:304–9. https://doi.org/10.1016/j.brainresbull.2005.06.035. Lohse, KR, Sherwood, DE, Healy, AF. Neuromuscular effects of shifting the focus of attention in a simple force production task. J Mot Behav 2011;43:173–84. https://doi.org/10.1080/00222895.2011.555436. Ay, AN, Dolukan, YB, Yildiz, MZ. The effect of attentional focus conditions on performer’s EMG activity. Acad Perspect Procedia 2019;1:240–7. https://doi.org/10.33793/acperpro.01.01.45. Wulf, G, Dufek, JS, Lozano, L, Pettigrew, C. Increased jump height and reduced EMG activity with an external focus. Hum Mov Sci 2010;29:440–8. https://doi.org/10.1016/j.humov.2009.11.008. Pan, L, Crouch, DL, Huang, H. Comparing EMG-based human-machine interfaces for estimating continuous, coordinated movements. IEEE Trans Neural Syst Rehabil Eng 2019;27:2145–54. https://doi.org/10.1109/tnsre.2019.2937929. Shi, WT, Lyu, ZJ, Tang, ST, Chia, TL, Yang, CY. A bionic hand controlled by hand gesture recognition based on surface EMG signals: a preliminary study. Biocybern Biomed Eng 2018;38:126–35. https://doi.org/10.1016/j.bbe.2017.11.001. Sharma, S, Dubey, AK. Movement control of robot in real time using EMG signal. In: ICPCES 2012-2012 2nd Int Conf Power. Control Embed Syst; 2012:1–4 pp. Shobhitha, AJ, Jegan, R, Melwin, AC. OWI-535 EDGE robotic arm control using ElectroMyoGram (EMG) signals. Int J Innov Technol Explor Eng 2013;2:282–6. https://www.ijitee.org/download/volume-2-issue-6/ number 60. Ay, AN, Yildiz, MZ. Mendeley data - data of EMG-based attentional focus experiments [Internet]. Mendeley Data 2020. Available from: https://data.mendeley.com/datasets/grmzmpvt4c/2 [Accessed 17 May 2020]. Singh, Y. Analaysis and classification of EMG signal using LabVIEW with different weights. [Master Thesis]. India: Thapar University; 2013. Tremolada, M, Taverna, L, Bonichini, S. Which factors influence attentional functions? attention assessed by KITAP in 105 6-to-10-year-old children. Basel: Behav Sci; 2019:9 p. Schücker, L, Parrington, L. Thinking about your running movement makes you less efficient: attentional focus effects on running economy and kinematics. J Sports Sci 2019;37:638–46. https://doi.org/10.1080/02640414.2018.1522697. Couvillion, KF, Fairbrother, JT. Expert and novice performers respond differently to attentional focus cues for speed jump roping. Front Psychol 2018;9:1–9. https://doi.org/10.3389/fpsyg.2018.02370. Daud, WMBW, Yahya, AB, Horng, CS, Sulaima, MF, Sudirman, R. Features extraction of electromyography signals in time domain on biceps brachii muscle. Int J Model Optim 2013;3:515–9. https://doi.org/10.7763/ijmo.2013.v3.332. Nazmi, N, Rahman, MAA, Yamamoto, SI, Ahmad, SA, Zamzuri, H, Mazlan, SA. A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors 2016;16:1–28. https://doi.org/10.3390/s16081304. Phinyomark, A, Khushaba, RN, Ibáñez-Marcelo, E, Patania, A, Scheme, E, Petri, G. Navigating features: a topologically informed chart of electromyographic features space. J R Soc Interface 2017;14:20170734. https://doi.org/10.1098/rsif.2017.0734. Ibrahimy, MI, Ahsan, MR, Khalifa, OO. Design and performance analysis of artificial neural network for hand motion detection from EMG signals. World Appl Sci J 2013;23:751–8. https://doi.org/10.5829/idosi.wasj.2013.23.06.117. Al-Timemy, AHA, Ghaeb, NH, Khalaf, TY. Wavelet neural network based Emg signal classifier. In: The 1st Regional Conference of Eng Sci NUCEJ; 2008:137–44 pp. Oweis, RJ, Rihani, R, Alkhawaja, A. ANN-based EMG classification for myoelectric control. Int J Med Eng Inf 2014;6:365. https://doi.org/10.1504/ijmei.2014.065442. Marchant, DC, Greig, M, Scott, C. Attentional focusing instructions influence force production and muscular activity during isokinetic elbow flexions. J Strength Condit Res 2009;23:2358–66. https://doi.org/10.1519/jsc.0b013e3181b8d1e5. Lohse, KR, Sherwood, DE, Healy, AF. How changing the focus of attention affects performance, kinematics, and electromyography in dart throwing. Hum Mov Sci 2010;29:542–55. https://doi.org/10.1016/j.humov.2010.05.001. Ardakani, ZP, Abdoli, B, Farsi, A, Ahmadi, A. The effect of attentional focus and manipulation of somatosensory on EMG of selected balance muscles in Elderly. Indian J Fundam Appl Life Sci. 2015;5:5528. https://www.sid.ir/en/Journal/JournalListPaper.aspx?ID=222466 number 12. Ashraf, R, Aghdasi, MT, Sayyah, M, Taghibiglo, N. The effects of internal and external focus of attention on children’s performance in vertical jump task. Int J Basic Sci Appl Res. 2012;1:1–5. https://dergipark.org.tr/tr/pub/turkjkin/issue/29954/326580. Mane, SM, Kambli, RA, Kazi, FS, Singh, NM. Hand motion recognition from single channel surface EMG using wavelet & artificial neural network. Procedia Comput Sci. 2015;49:58–65. https://doi.org/10.1016/j.procs.2015.04.227. Duan, F, Dai, L, Chang, W, Chen, Z, Zhu, C, Li, W. SEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform. IEEE Trans Ind Electron 2016;63:1923–34. https://doi.org/10.1109/tie.2015.2497212. Kehri, V, Ingle, R, Awale, R, Oimbe, S. Techniques of EMG signal analysis and classification of neuromuscular diseases. Adv Intell Syst Res. 2017;137:485–91. https://doi.org/10.2991/iccasp-16.2017.71. Oleinikov, A, Abibullaev, B, Shintemirov, A, Folgheraiter, M. Feature extraction and real-time recognition of hand motion intentions from EMGs via artificial neural networks. In: 6th International conference on brain-computer interface IEEE; 2018:1–5 pp. |
| فهرسة مساهمة: | Keywords: EMG classification; attentional focus; internal focusing |
| تواريخ الأحداث: | Date Created: 20201016 Date Completed: 20210825 Latest Revision: 20210825 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1515/bmt-2020-0082 |
| PMID: | 33064666 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1862-278X |
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| DOI: | 10.1515/bmt-2020-0082 |