Skip to main content

Advertisement

SpringerLink
Log in

A reliable and efficient machine learning pipeline for american sign language gesture recognition using EMG sensors

  • Track 3: Biometrics and HCI
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Sign languages has extensive applications among differently-abled to communicate with their surroundings. With the development of different sensing technologies, several new human-computer interaction techniques (HCI) have been established to recognize hand gestures. Computer vision-based methods have shown significant utility for such applications. However, these methods are strongly dependent on the lighting conditions. The surface electromyography (sEMG) technique is invariant to lighting conditions and can easily reflect human motion intention. In this work, sEMG based sign language recognition model was developed using an efficient machine learning pipeline.

Two sEMG datasets were recorded for predefined hand gestures using wireless sensors. These signals were mainly acquired against 24 manual alphabets (ASL-24) and ten digits(ASL-10) of American Sign Language (ASL). The collected data sets were preprocessed, and around 450 well-established feature was extracted from each sEMG channel. We applied an ensemble feature selection approach combining four diverse filter-based feature selection methods (ANOVA, Chi-square, Mutual Info, ReliefF). A newly proposed feature combiner that exploits feature–feature and feature–class correlation thresholds is used to combine feature subsets formed across the ensemble. The resulting features comprise reduced & most representative feature subsets and are further used in the pipeline for classifying ASL gestures.

Using the CatBoost algorithm, the pipeline presented excellent average classification accuracy(99.91% on ASL-24) and other performance parameters for recognizing ASL gestures. The pipeline was also applied and validated on a benchmark dataset (Ninapro database 5, exercise A) and achieved similar outcomes. The result highlights the feasibility of using sEMG based approach as better options to computer-vision-based techniques to build an accurate and robust Sign Language Recognition system (SLRS). Moreover, efforts were made to find the optimal number of sensors and features for recognition task on (ASL-10 dataset) without impacting the overall reliability and accuracy of the system. The experiments results can be used to enhance the performance of various wearable sEMG sensor based HCI applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Algorithm 1
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Aboy M, Hornero R, Abásolo D, Álvarez D (2006) Interpretation of the lempel-ziv complexity measure in the context of biomedical signal analysis. IEEE Trans Biomed Eng 53(11):2282–2288

    Google Scholar 

  2. Ahmed MA, Zaidan BB, Zaidan AA, Salih MM, Lakulu MMB (2018) A review on systems-based sensory gloves for sign language recognition state of the art between 2007 and 2017. Sensors 18(7):2208

    Google Scholar 

  3. Ahsan MR, Ibrahimy MI, Khalifa OO et al (2009) Emg signal classification for human computer interaction: a review. Eur J Sci Res 33(3):480–501

    Google Scholar 

  4. Ali Khan S, Hussain A, Basit A, Akram S (2014) Kruskal-wallis-based computationally efficient feature selection for face recognition. Sci World J, vol 2014

  5. Anderson R, Wiryana F, Ariesta MC, Kusuma GP et al (2017) Sign language recognition application systems for deaf-mute people: a review based on input-process-output. Procedia Comput Sci 116:441–448

    Google Scholar 

  6. Atzori M, Gijsberts A, Castellini C, Caputo B, Hager A-GM, Elsig S, Giatsidis G, Bassetto F, Müller H (2014) Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci Data 1(1):1–13

    Google Scholar 

  7. Barbhuiya AA, Karsh RK, Jain R (2021) Cnn based feature extraction and classification for sign language. Multimed Tools Appl 80(2):3051–3069

    Google Scholar 

  8. Batista GE, Keogh EJ, Tataw OM, De Souza VM (2014) Cid: an efficient complexity-invariant distance for time series. Data Min Knowl Disc 28 (3):634–669

    MathSciNet  MATH  Google Scholar 

  9. Battison R (1978) Lexical borrowing in american sign language

  10. Bheda V, Radpour D (2017) Using deep convolutional networks for gesture recognition in american sign language. arXiv:1710.06836

  11. Bin Munir M, Alam FR, Ishrak S, Hussain S, Shalahuddin M, Islam MN (2021) A machine learning based sign language interpretation system for communication with deaf-mute people. In: Proceedings of the XXI international conference on human computer interaction, pp 1–9

  12. Blum AL, Langley P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97(1-2):245–271

    MathSciNet  MATH  Google Scholar 

  13. Cardenas EJE, Chavez GC (2020) Multimodal hand gesture recognition combining temporal and pose information based on cnn descriptors and histogram of cumulative magnitudes. J Vis Commun Image Represent 71:102772

    Google Scholar 

  14. Chang Y-W, Lin C-J (2008) Feature ranking using linear svm. In: Causation and prediction challenge. PMLR, pp 53–64

  15. Chen C-W, Tsai Y-H, Chang F-R, Lin W-C (2020) Ensemble feature selection in medical datasets: combining filter, wrapper, and embedded feature selection results. Expert Syst 37(5):12553

    Google Scholar 

  16. Chicco D, Jurman G (2020) The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genomics 21(1):1–13

    Google Scholar 

  17. Chowdhury RH, Reaz MB, Ali MABM, Bakar AA, Chellappan K, Chang T (2013) Surface electromyography signal processing and classification techniques. Sensors (Basel Switzerland) 13(9):12431–12466

    Google Scholar 

  18. Chuan C-H, Regina E, Guardino C (2014) American sign language recognition using leap motion sensor. In: 2014 13th International conference on machine learning and applications. IEEE, pp 541–544

  19. Cooper H, Holt B, Bowden R (2011) Sign language recognition. In: Visual analysis of humans. Springer, pp 539–562

  20. Day S (2002) Important factors in surface emg measurement. Bortec Biomed Ltd Pub:1–17

  21. De la Rosa R, Alonso A, Carrera A, Durán R, Fernández P (2010) Man-machine interface system for neuromuscular training and evaluation based on emg and mmg signals. Sensors (Basel Switzerland) 10(12):11100–11125

    Google Scholar 

  22. Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computat 10(7):1895–1923

    Google Scholar 

  23. Dinno A (2015) Nonparametric pairwise multiple comparisons in independent groups using dunn’s test. Stata J 15(1):292–300

    Google Scholar 

  24. Dorogush AV, Ershov V, Gulin A (2018) Catboost: gradient boosting with categorical features support. arXiv:1810.11363

  25. Dunn OJ (1964) Multiple comparisons using rank sums. Technometrics 6(3):241–252

    Google Scholar 

  26. Erkilinc MS, Sahin F (2011) Camera control with emg signals using principal component analysis and support vector machines. In: 2011 IEEE international systems conference. IEEE, pp 417–421

  27. Fatmi R, Rashad S, Integlia R (2019) Comparing ann, svm, and hmm based machine learning methods for american sign language recognition using wearable motion sensors. In: 2019 IEEE 9th annual computing and communication workshop and conference (CCWC)

  28. Fels SS, Hinton GE (1993) Glove-talk: a neural network interface between a data-glove and a speech synthesizer. IEEE Trans Neural Netw 4(1):2–8

    Google Scholar 

  29. Feng Y, Uchidiuno UA, Zahiri HR, George I, Park AE, Mentis H (2021) Comparison of kinect and leap motion for intraoperative image interaction. Surg Innov 28(1):33–40

    Google Scholar 

  30. Ferri C, Hernández-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recognit Lett 30 (1):27–38

    Google Scholar 

  31. Friedrich R, Siegert S, Peinke J, Siefert M, Lindemann M, Raethjen J, Deuschl G, Pfister G et al (2000) Extracting model equations from experimental data. Phys Lett A 271(3):217–222

    Google Scholar 

  32. Garcia B, Viesca SA (2016) Real-time american sign language recognition with convolutional neural networks. Convolutional Neural Netw Vis Recognit 2:225–232

    Google Scholar 

  33. Genuer R, Poggi J-M, Tuleau-Malot C (2015) Vsurf: an r package for variable selection using random forests. R Journal 7(2):19–33

    Google Scholar 

  34. Gomez-Donoso F, Orts-Escolano S, Cazorla M (2019) Accurate and efficient 3d hand pose regression for robot hand teleoperation using a monocular rgb camera. Expert Syst Appl 136:327–337

    Google Scholar 

  35. Goswami T, Javaji SR (2021) Cnn model for american sign language recognition. In: ICCCE 2020. Springer, pp 55–61

  36. Grandini M, Bagli E, Visani G (2020) Metrics for multi-class classification: an overview. arXiv:2008.05756

  37. Güler NF, Koçer S (2005) Classification of emg signals using pca and fft. J Med Syst 29(3):241–250

    Google Scholar 

  38. Guo D, Zhou W, Li H, Wang M (2018) Hierarchical lstm for sign language translation. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

  39. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157–1182

    MATH  Google Scholar 

  40. Haq AU, Zhang D, Peng H, Rahman SU (2019) Combining multiple feature-ranking techniques and clustering of variables for feature selection. IEEE Access 7:151482–151492. https://doi.org/10.1109/ACCESS.2019.2947701

    Google Scholar 

  41. Hoque N, Singh M, Bhattacharyya DK (2018) Efs-mi: an ensemble feature selection method for classification. Complex Intell Syst 4(2):105–118

    Google Scholar 

  42. Hudgins B, Parker P, Scott RN (1993) A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 40(1):82–94

    Google Scholar 

  43. Isaacs J, Foo S (2004) Hand pose estimation for american sign language recognition. In: Thirty-sixth southeastern symposium on system theory, 2004. Proceedings of the. IEEE, pp 132–136

  44. Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158. https://doi.org/10.1109/34.574797

    Google Scholar 

  45. Jones E, Oliphant T, Peterson P et al (2001) SciPy: open source scientific tools for python. http://www.scipy.org/. Accessed 10 June 2022

  46. Jurman G, Riccadonna S, Furlanello C (2012) A comparison of mcc and cen error measures in multi-class prediction

  47. Kadhim RA, Khamees M (2020) A real-time american sign language recognition system using convolutional neural network for real datasets. TEM J 9 (3):937

    Google Scholar 

  48. Kanoga S, Kanemura A, Asoh H (2020) Are armband semg devices dense enough for long-term use?—sensor placement shifts cause significant reduction in recognition accuracy. Biomed Signal Process Contr 60:101981

    Google Scholar 

  49. Kerber F, Schardt P, Löchtefeld M (2015) Wristrotate: a personalized motion gesture delimiter for wrist-worn devices. In: Proceedings of the 14th international conference on mobile and ubiquitous multimedia, pp 218–222

  50. Khan SM, Khan AA, Farooq O (2019) Selection of features and classifiers for emg-eeg-based upper limb assistive devices—a review. IEEE Rev Biomed Eng 13:248–260

    Google Scholar 

  51. Kleiman R, Page D (2019) Aucμ: a performance metric for multi-class machine learning models. In: International conference on machine learning. PMLR, pp 3439–3447

  52. Koller O (2020) Quantitative survey of the state of the art in sign language recognition. arXiv:2008.09918

  53. Kosmidou VE, Hadjileontiadis LJ, Panas SM (2006) Evaluation of surface emg features for the recognition of american sign language gestures. In: 2006 International conference of the IEEE engineering in medicine and biology society, pp 6197–6200. https://doi.org/10.1109/IEMBS.2006.259428

  54. Kuroda T, Tabata Y, Goto A, Ikuta H, Murakami M et al (2004) Consumer price data-glove for sign language recognition. In: Proceeding ICDVRAT, pp 253–258

  55. Lee CK, Ng KK, Chen C-H, Lau HC, Chung S, Tsoi T (2021) American sign language recognition and training method with recurrent neural network. Expert Syst Appl 167:114403

    Google Scholar 

  56. Li L, Jiang S, Shull PB, Gu G (2018) Skingest: artificial skin for gesture recognition via filmy stretchable strain sensors. Adv Robot 32 (21):1112–1121

    Google Scholar 

  57. Liddell SK, Johnson RE (1989) American sign language: the phonological base. Sign Language Studies 64(1):195–277

    Google Scholar 

  58. Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems 30. Curran Associates, Inc., pp 4765–4774. http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf. Accessed 10 June 2022

  59. Masood S, Srivastava A, Thuwal HC, Ahmad M (2018) Real-time sign language gesture (word) recognition from video sequences using cnn and rnn. In: Intelligent engineering informatics. Springer, pp 623–632

  60. McKight PE, Najab J (2010) Kruskal-wallis test. corsini Encyclo Psychol:1–1

  61. Mehdi SA, Khan YN (2002) Sign language recognition using sensor gloves. In: Proceedings of the 9th international conference on neural information processing, 2002. ICONIP’02., vol 5, pp 2204–22065. https://doi.org/10.1109/ICONIP.2002.1201884

  62. Miften FS, Diykh M, Abdulla S, Siuly S, Green JH, Deo RC (2021) A new framework for classification of multi-category hand grasps using emg signals. Artif Intell Med 112:102005

    Google Scholar 

  63. Müller M (2007) Information retrieval for music and motion. Springer, vol 2

  64. Munib Q, Habeeb M, Takruri B, Al-Malik HA (2007) American sign language (asl) recognition based on hough transform and neural networks. Expert Syst Appl 32(1):24–37

    Google Scholar 

  65. NIDCD (2021) American sign language. https://www.nidcd.nih.gov/health/american-sign-language. Accessed 26 May 2021

  66. Nishad A, Upadhyay A, Pachori RB, Acharya UR (2019) Automated classification of hand movements using tunable-q wavelet transform based filter-bank with surface electromyogram signals. Futur Gener Comput Syst 93:96–110

    Google Scholar 

  67. Olsson JOS, Oard DW (2006) Combining feature selectors for text classification. In: Proceedings of the 15th ACM international conference on information and knowledge management, pp 798–799

  68. Oz C, Leu MC (2005) Recognition of finger spelling of american sign language with artificial neural network using position/orientation sensors and data glove. In: International symposium on neural networks. Springer, pp 157–164

  69. Oz C, Leu MC (2007) Linguistic properties based on american sign language isolated word recognition with artificial neural networks using a sensory glove and motion tracker. Neurocomputing 70(16-18):2891–2901

    Google Scholar 

  70. Oz C, Leu MC (2011) American sign language word recognition with a sensory glove using artificial neural networks. Eng Appl Artif Intell 24 (7):1204–1213

    Google Scholar 

  71. Paudyal P, Banerjee A, Gupta SK (2016) Sceptre: a pervasive, non-invasive, and programmable gesture recognition technology. In: Proceedings of the 21st international conference on intelligent user interfaces, pp 282–293

  72. Pires R, Falcari T, Campo AB, Pulcineli BC, Hamill J, Ervilha UF (2019) Using a support vector machine algorithm to classify lower-extremity emg signals during running shod/unshod with different foot strike patterns. J Appl Biomechan 35(1):87–90

    Google Scholar 

  73. Pizzolato S, Tagliapietra L, Cognolato M, Reggiani M, Müller H, Atzori M (2017) Comparison of six electromyography acquisition setups on hand movement classification tasks. PloS One 12(10):0186132

    Google Scholar 

  74. Poizner H, Tallal P (1987) Temporal processing in deaf signers. Brain Lang 30(1):52–62

    Google Scholar 

  75. Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) Catboost: unbiased boosting with categorical features. In: Advances in neural information processing systems, pp 6638–6648

  76. Pugeault N, Bowden R (2011) Spelling it out: real-time asl fingerspelling recognition. In: 2011 IEEE international conference on computer vision workshops (ICCV workshops). IEEE, pp 1114–1119

  77. Rao GA, Syamala K, Kishore P, Sastry A (2018) Deep convolutional neural networks for sign language recognition. In: 2018 Conference on signal processing and communication engineering systems (SPACES). IEEE, pp 194–197

  78. Rashid O, Al-Hamadi A, Michaelis B (2010) Utilizing invariant descriptors for finger spelling american sign language using svm. In: International symposium on visual computing. Springer, pp 253–263

  79. Rastgoo R, Kiani K, Escalera S (2018) Multi-modal deep hand sign language recognition in still images using restricted boltzmann machine. Entropy 20(11):809

    Google Scholar 

  80. Rastgoo R, Kiani K, Escalera S (2020) Hand sign language recognition using multi-view hand skeleton. Expert Syst Appl 150:113336

    Google Scholar 

  81. Remeseiro B, Bolon-Canedo V (2019) A review of feature selection methods in medical applications. Comput Bio Med 112:103375

    Google Scholar 

  82. Rivera-Acosta M, Ruiz-Varela JM, Ortega-Cisneros S, Rivera J, Parra-Michel R, Mejia-Alvarez P (2021) Spelling correction real-time american sign language alphabet translation system based on yolo network and lstm. Electronics 10(9):1035

    Google Scholar 

  83. Rodríguez-Tapia B, Soto I, Martínez DM, Arballo NC (2020) Myoelectric interfaces and related applications: current state of emg signal processing–a systematic review. IEEE Access 8:7792–7805

    Google Scholar 

  84. Salo F, Injadat M, Moubayed A, Nassif AB, Essex A (2019) Clustering enabled classification using ensemble feature selection for intrusion detection. In: 2019 International conference on computing, networking and communications (ICNC). IEEE, pp 276–281

  85. Savur C, Sahin F (2015) Real-time american sign language recognition system using surface emg signal. In: 2015 IEEE 14th international conference on machine learning and applications (ICMLA). IEEE, pp 497–502

  86. Savur C, Sahin F (2016) American sign language recognition system by using surface emg signal. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 002872–002877

  87. Schreiber T, Schmitz A (1997) Discrimination power of measures for nonlinearity in a time series. Phys Rev E 55(5):5443

    Google Scholar 

  88. Sharma R, Pachori RB (2015) Classification of epileptic seizures in eeg signals based on phase space representation of intrinsic mode functions. Expert Syst Appl 42(3):1106–1117

    Google Scholar 

  89. Simons EDMGF, Fennig CD (2021) Ethnologue: languages of the world. http://www.ethnologue.com. Accessed 26 May 2021

  90. Starner T, Pentland A (1997) Real-time american sign language recognition from video using hidden markov models. In: Motion-based recognition. Springer, pp 227–243

  91. Sun C, Zhang T, Bao B-K, Xu C (2013) Latent support vector machine for sign language recognition with kinect. In: 2013 IEEE international conference on image processing. IEEE, pp 4190–4194

  92. Taylor J (2016) Real-time translation of american sign language using wearable technology

  93. Too J, Abdullah A, Saad NM, Ali NM, Musa H (2018) A detail study of wavelet families for emg pattern recognition. Int J Electr Comput Eng (IJECE) 8(6):4221–4229

    Google Scholar 

  94. Van der Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res, vol 9(11)

  95. Wadhawan A, Kumar P (2019) Sign language recognition systems: a decade systematic literature review. Arch Computat Methods Eng:1–29

  96. Wadhawan A, Kumar P (2020) Deep learning-based sign language recognition system for static signs. Neural Comput Appl 32(12):7957–7968

    Google Scholar 

  97. Wang H, Khoshgoftaar TM, Napolitano A (2012) Software measurement data reduction using ensemble techniques. Neurocomputing 92:124–132

    Google Scholar 

  98. Wattenberg M, Viégas F, Johnson I (2016) How to use t-sne effectively. Distill. https://doi.org/10.23915/distill.00002https://doi.org/10.23915/distill.00002

  99. Wu J, Sun L, Jafari R (2016) A wearable system for recognizing american sign language in real-time using imu and surface emg sensors. IEEE J Biomed Health Inform 20(5):1281–1290. https://doi.org/10.1109/JBHI.2016.2598302

    Google Scholar 

  100. Wu J, Tian Z, Sun L, Estevez L, Jafari R (2015) Real-time american sign language recognition using wrist-worn motion and surface emg sensors. In: 2015 IEEE 12th international conference on wearable and implantable body sensor networks (BSN). IEEE, pp 1–6

  101. Wu J, Tian Z, Sun L, Estevez L, Jafari R (2015) Real-time american sign language recognition using wrist-worn motion and surface emg sensors. In: 2015 IEEE 12th international conference on wearable and implantable body sensor networks (BSN). https://doi.org/10.1109/BSN.2015.7299393, pp 1–6

  102. Yeh C-CM, Zhu Y, Ulanova L, Begum N, Ding Y, Dau HA, Silva DF, Mueen A, Keogh E (2016) Matrix profile i: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: 2016 IEEE 16th international conference on data mining (ICDM). Ieee, pp 1317–1322

  103. Yu E, Cho S (2006) Ensemble based on ga wrapper feature selection. Comput Industr Eng 51(1):111–116

    Google Scholar 

  104. Zafrulla Z, Brashear H, Starner T, Hamilton H, Presti P (2011) American sign language recognition with the kinect. In: Proceedings of the 13th international conference on multimodal interfaces, pp 279–286

  105. Zamani M, Kanan HR (2014) Saliency based alphabet and numbers of american sign language recognition using linear feature extraction. In: 2014 4th International conference on computer and knowledge engineering (ICCKE). IEEE, pp 398–403

  106. Zhang J, Bi H, Chen Y, Wang M, Han L, Cai L (2019) Smarthandwriting: handwritten chinese character recognition with smartwatch. IEEE Internet Things J 7(2):960–970

    Google Scholar 

  107. Zhang Y, Gong D-W, Cheng J (2017) Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans Computat Bio Bioinform 14(1):64–75. https://doi.org/10.1109/TCBB.2015.2476796

    Google Scholar 

  108. Zhao W (2016) A concise tutorial on human motion tracking and recognition with microsoft kinect. Sci China Inf Sci 59(9):1–5

    Google Scholar 

  109. Zheng M, Crouch M, Eggleston MS (2021) Surface electromyography as a natural human-machine interface: a review. arXiv:2101.04658

  110. Zia ur Rehman M, Gilani SO, Waris A, Niazi IK, Slabaugh G, Farina D, Kamavuako EN (2018) Stacked sparse autoencoders for emg-based classification of hand motions: a comparative multi day analyses between surface and intramuscular emg. Appl Sci 8(7):1126

    Google Scholar 

Download references

Acknowledgements

We thank Dr. Shiru Sharma, Associate Professor, School of Biomedical Engineering IIT BHU, for providing the MyoArmband for data collection. We also thank Dr. Alok Prakash, School of Biomedical Engineering IIT BHU, for help in deciding the experimental protocol for data collection.

Author information

Authors and Affiliations

  1. CSE Deptt., Indian Institute of Technology (BHU), Varanasi, 221005, U.P., India

    Shashank Kumar Singh & Amrita Chaturvedi

Authors
  1. Shashank Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amrita Chaturvedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shashank Kumar Singh.

Ethics declarations

Conflict of Interests

None of the authors have any conflict of interest to declare.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, S.K., Chaturvedi, A. A reliable and efficient machine learning pipeline for american sign language gesture recognition using EMG sensors. Multimed Tools Appl 82, 23833–23871 (2023). https://doi.org/10.1007/s11042-022-14117-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14117-y

Keywords

Search

Navigation