IMO-ISHORA TILINI TANIB OLISH: USULLAR VA MODELLAR TAHLILI
Keywords:
imo-ishora tilini aniqlash, elektromiografiya, qo‘l imo-ishoralarini aniqlash, tanib olish aniqligi, xususiyatlarni ajratib olish va tasniflashAbstract
Ushbu maqolada imo-ishora tilini elektromiografik signallari asosida tanib olish sohasidagi asosiy usullar va modellar tahlil qilinib, imo-ishora tilining turli modellari va ularning o‘zaro farqlari batafsil ko‘rib chiqilgan. Shuningdek, EMG signallarini qayd etuvchi qurilmalar tasnifi keltirilib, ularning texnik xususiyatlari taqqoslangan. Maqola doirasida imo-ishora tilini tanib olishga oid ilmiy adabiyotlarning yillar kesimidagi rivojlanishi tahlili amalga oshirilgan. EMG orqali imo-ishora tilini tanib olish metodlarining qo‘llanilishi tahlili ushbu sohaning zamonaviy tendensiyalarini ko‘rsatadi. Mazkur tahlillar imo-ishora tilini tanib olishda samarali modellarni aniqlash va ularni real amaliyotga tatbiq etishda muhim ilmiy asos yaratadi.
References
https://www.gazeta.uz/oz/2020/09/24/sign-language/
A. S. M. Miah, J. Shin, M. A. M. Hasan, Y. Okuyama, and A. Nobuyoshi, ‘‘Dynamic hand gesture recognition using effective feature extraction and attention based deep neural network,’’ in Proc. IEEE 16th Int. Symp. Embedded Multicore/ Many-Core Syst.-Chip (MCSoC), Kuala Lumpur, Malaysia, Dec. 2023, pp. 241–247.
A. S. M. Miah, M. A. M. Hasan, Y. Tomioka, and J. Shin, ‘‘Hand gesture recognition for multi-culture sign language using graph and general deep learning network,’’ IEEE Open J. Comput. Soc., vol. 5, pp. 144–155, 2024.
D. Sarma and M. K. Bhuyan, ‘‘Methods, databases and recent advancement of vision-based hand gesture recognition for HCI systems: A review,’’ Social Netw. Comput. Sci., vol. 2, no. 6, p. 436, Nov. 2021.
Jungpil Shin, Abu Saleh Musa Miah, Humaun Kabir, Abdur Rahim, Abdullah Al Shiam. A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities. 9 September 2024 Digital Object Identifier 10.1109/ACCESS.2024.3456436
N. Jiang, S. Dosen, K. R. Múller, and D. Farina, ‘‘Myoelectric control of artificial limbs-is there a need to change focus,’’ IEEE Signal Process. Mag., vol. 29, no. 5, pp. 150–152, May 2012.
A. S. M. Miah, M. R. Islam, and M. K. I. Molla, ‘‘EEG classification for MI-BCI using CSP with averaging covariance matrices: An experimental study,’’ in Proc. Int. Conf. Comput., Commun., Chem., Mater. Electron. Eng. (IC4ME2), vol. 59, Jul. 2019, pp. 1–5.
M. M. H. Joy, M. Hasan, A. S. M. Miah, A. Ahmed, S. A. Tohfa,
M. F. I. Bhuaiyan, A. Zannat, and M. M. Rashid, ‘‘Multiclass MI-task classification using logistic regression and filter bank common spatial patterns,’’ in Proc. Int. Conf. Comput. Sci., Commun. Secur. Singapore: Springer, 2020, pp. 160–170
A. S. M. Miah, M. A. Rahim, and J. Shin, ‘‘Motor-imagery classification using Riemannian geometry with median absolute deviation,’’ Electronics, vol. 9, no. 10, p. 1584, Sep. 2020.
M. Montazerin, E. Rahimian, F. Naderkhani, S. F. Atashzar,
S. Yanushkevich, and A. Mohammadi, ‘‘Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals,’’ Sci. Rep., vol. 13, no. 1, p. 11000, Jul. 2023
G. Li, D. Bai, G. Jiang, D. Jiang, J. Yun, Z. Yang, and Y. Sun,
‘‘Continuous dynamic gesture recognition using surface EMG signals
based on blockchain-enabled Internet of Medical Things,’’ Inf. Sci., vol. 646, Oct. 2023, Art. no. 119409
Mills, K.R. The basics of electromyography. J. Neurol. Neurosurg. Psychiatry 2005, 76, ii32–ii35. [CrossRef] [PubMed]
Mizrahi, J. Advances in Applied Electromyography; IntechOpen: London, UK, 2011; ISBN 978-953-307-382-8. Available online: https://www.intechopen.com/books/359 (accessed on 20 December 2022). [CrossRef]
Flaxman, T.E.; Alkjaer, T.; Smale, K.B.; Simonsen, E.B.; Krogsgaard, M.R.; Benoit, D.L. Differences in EMG–moment relationships between ACL-injured and uninjured adults during a weight-bearing multidirectional force control task. J. Orthop. Res. 2019, 37, 113–123. [CrossRef] [PubMed]
Wu, C.W.; Liu, X.; Barczy´nski, M.; Kim, H.Y.; Dionigi, G.; Sun, H.; Chiang, F.Y.; Kamani, D.; Randolph, G.W. Optimal stimulation during monitored thyroid surgery: EMG response characteristics in a porcine model. Laryngoscope 2017, 127, 998–1005. [CrossRef] [PubMed]
Fukuhara, S.; Kawashima, T.; Oka, H. Indices reflecting muscle contraction performance during exercise based on a combined electromyography and mechanomyography approach. Sci. Rep. 2021, 11, 21208. [CrossRef] [PubMed]
Ficek, K.; Gołas, A.; Pietraszewski, P.; Strózik, M.; Krzysztofik, M. The Effects of a Combined Pre- and Post-Operative Anterior Cruciate Ligament Reconstruction Rehabilitation Program on Lower Extremity Muscle Imbalance. Appl. Sci. 2022, 12, 7411. [CrossRef]
Lee, K. EMG-Triggered Pedaling Training on Muscle Activation, Gait, and Motor Function for Stroke Patients. Brain Sci. 2022, 12, 76. [CrossRef] [PubMed]
Osborne, J.A.; Botkin, R.; Colon-Semenza, C.; DeAngelis, T.R.; Gallardo, O.G.; Kosakowski, H.; Martello, J.; Pradhan, S.; Rafferty, M.; Readinger, J.L.; et al. Physical therapist management of parkinson disease: A clinical practice guideline from the American Physical Therapy Association. Phys. Ther. 2021, 102, pzab302. [CrossRef] [PubMed]
J. Pu, W. Zhou, and H. Li, ‘‘Iterative alignment network for continuous sign language recognition,’’ in Proc. IEEE/CVF Conf. Comput. Vis Pattern Recognit., Jun. 2019, pp. 4165–4174.
A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo,
T. Nayak, A. Andreopoulos, G. Garreau, M. Mendoza, J. Kusnitz, M. Debole, S. Esser, T. Delbruck, M. Flickner, and D. Modha, ‘‘A low power, fully event-based gesture recognition system,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 7388–7397
S. Zhao, W. Yang, and Y. Wang, ‘‘A new hand segmentation method based on fully convolutional network,’’ in Proc. Chin. Control Decis. Conf. (CCDC), Shenyang, China, Jun. 2018, pp. 5966–5970
D. Li, C. R. Opazo, X. Yu, and H. Li, ‘‘Word-level deep sign
language recognition from video: A new large-scale dataset and methods
comparison,’’ in Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), Mar. 2020, pp. 1448–1458
O. M. Sincan and H. Y. Keles, ‘‘AUTSL: A large scale multi-modal
Turkish sign language dataset and baseline methods,’’ IEEE Access, vol. 8, pp. 181340–181355, 2020
S. Jiang, B. Sun, L. Wang, Y. Bai, K. Li, and Y. Fu, ‘‘Skeleton aware multimodal sign language recognition,’’ in Proc. IEEE/CVF Conf. Comput. Vis.
Pattern Recognit. Workshops (CVPRW), Jun. 2021, pp. 3413–3423.
F. Stival, S. Michieletto, M. Cognolato, E. Pagello, H. Müller, and
M. Atzori, ‘‘A quantitative taxonomy of human hand grasps,’’ J. NeuroEng. Rehabil., vol. 16, no. 1, pp. 1–17, Dec. 2019.
E. Kim, J. Shin, Y. Kwon, and B. Park, ‘‘EMG-based dynamic hand gesture recognition using edge AI for human–robot interaction,’’Electronics, vol. 12, no. 7, p. 1541, Mar. 2023.
K. H. Lee, J. Y. Min, and S. Byun, ‘‘Electromyogram-based classification of hand and finger gestures using artificial neural networks,’’ Sensors, vol. 22, no. 1, p. 225, Dec. 2021.
J. G. C. Alfaro and A. L. Trejos, ‘‘User-independent hand gesture
recognition classification models using sensor fusion,’’ Sensors, vol. 22, no. 4, p. 1321, Feb. 2022
W. Wei, Y. Wong, Y. Du, Y. Hu, M. Kankanhalli, and W. Geng,
‘‘A multi-stream convolutional neural network for EMG-based gesture recognition in muscle-computer interface,’’ Pattern Recognit. Lett., vol. 119, pp. 131–138, Mar. 2019.
D. Esposito, E. Andreozzi, G. D. Gargiulo, A. Fratini, G. D’Addio,
G. R. Naik, and P. Bifulco, ‘‘A piezoresistive array armband with reduced number of sensors for hand gesture recognition,’’ Frontiers Neurorobotics, vol. 13, p. 114, Jan. 2020.
Kim, S.; Kim, J.; Ahn, S.; Kim, Y. Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors. Technol. Health Care 2018, 26, 249–258. [CrossRef]
Jane, S.P.Y.; Sasidhar, S. Sign language interpreter: Classification of forearm emg and imu signals for signing exact english. In Proceedings of the 2018 IEEE 14Th International Conference on Control and Automation (ICCA), Anchorage, AK, USA, 12–15 June 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 947–952.
Kim, J.; Wagner, J.; Rehm, M.; André, E. Bi-channel sensor fusion for automatic sign language recognition. In Proceedings of the 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, Amsterdam, The Netherlands, 17–19 September 2008; IEEE: Piscataway, NJ, USA, 2008;
Amor, A.B.H.; El Ghoul, O.; Jemni, M. Sign language handshape recognition using Myo Armband. In Proceedings of the 2019 7th International Conference on ICT & Accessibility (ICTA), Hammamet, Tunisia, 13–15 December 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–5.
Derr, C.; Sahin, F. Signer-independent classification of American sign language word signs using surface EMG. In Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada, 5–8 October 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 665–670.
Divya, B.; Delpha, J.; Badrinath, S. Public speaking words (Indian sign language) recognition using EMG. In Proceedings of the 2017 International Conference on Smart Technologies for Smart Nation (SmartTechCon), Bengaluru, India, 17–19 August 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 798–800.
Zhang, X.; Chen, X.; Li, Y.; Lantz, V.; Wang, K.; Yang, J. A framework for hand
gesture recognition based on accelerometer and EMG sensors. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2011, 41, 1064–1076. [CrossRef]
M. Zia ur Rehman, A. Waris, S. O. Gilani, M. Jochumsen, I. K. Niazi,
M. Jamil, D. Farina, and E. N. Kamavuako, ‘‘Multiday EMG-based classification of hand motions with deep learning techniques,’’ Sensors, vol. 18, no. 8, p. 2497, Aug. 2018.
P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen, and A. Skodras, ‘‘Improved gesture recognition based on sEMG signals and TCN,’’ in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), May 2019, pp. 1169–1173.
A. S. M. Miah, M. A. M. Hasan, and J. Shin, ‘‘Dynamic hand gesture recognition using multi-branch attention-based graph and general deep learning model,’’ IEEE Access, vol. 11, pp. 4703–4716, 2023.
S. Zabihi, E. Rahimian, A. Asif, and A. Mohammadi, ‘‘TraHGR: Transformer for hand gesture recognition via electromyography,’’ IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 4211–4224, 2023.
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Copyright (c) 2025 Qudratjon Rafiqovich Zohirov, Mamadiyor Egamberdiyevich Sattorov, Sardor Xoliqul o‘g‘li Boyqobilov, Mirjaxon Mirzoxid o‘g‘li Temirov, Feruz Yusufboy o‘g‘li Ro‘ziboyev, Quvonchbek Geldiyor o‘g‘li Madatov

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