Evaluation of Models for Gesture Recognition from Biometric Signals of a Person with Reduced Mobility
Abstract
This paper compares the results of three computational models (pattern recognition, hidden Markov models, and bag of features) for recognizing the hand gestures of a user with reduced mobility using biometric signal processing. The evaluation of the models included eight gestures co-designed with a person with reduced mobility. The models were evaluated using a cross-validation scheme, calculating sensitivity and precision metrics, and a data set of ten repetitions of each gesture. It can be concluded that the bag-of-features model achieved the best performance considering the two metrics under evaluation; the traditional pattern recognition model, using vector support machines, produced the most stable results; and the hidden Markov models had the lowest performance.
References
D. A. Norman, “The way I see it: Natural user interfaces are not natural,” interactions, vol. 17, no. 3, pp. 6-10, May. 2010. https://doi.org/10.1145/1744161.1744163
A. Jaimes y N. Sebe, “Multimodal human–computer interaction: A survey,” Computer Vision and Image Understanding, vol. 108, no. 1–2, pp. 116–134, Oct. 2007. https://doi.org/10.1016/j.cviu.2006.10.019
C. Manresa, J. Varona, R. Mas, y F. J. Perales, “Hand Tracking and Gesture Recognition for Human-Computer Interaction”, ELCVIA Electron. Lett. Comput. Vis. Image Anal., vol. 5, no. 3, pp. 96-104, Nov. 2005. https://doi.org/10.5565/rev/elcvia.109
S. S. Rautaray y A. Agrawal, “Vision based hand gesture recognition for human computer interaction: a survey,” Artificial Intelligence Review, vol. 43, no. 1, pp. 1–54, Jan. 2015. https://doi.org/10.1007/s10462-012-9356-9
P. K. Pisharady y M. Saerbeck, “Recent methods and databases in vision-based hand gesture recognition: A review,” Computer Vision and Image Understanding, vol. 141, pp. 152–165, Dec. 2015. https://doi.org/10.1016/j.cviu.2015.08.004
M. J. Cheok, Z. Omar, y M. H. Jaward, “A review of hand gesture and sign language recognition techniques,” International Journal of Machine Learning and Cybernetics, vol. 10, no. 1, pp. 131–153, Jan. 2019. https://doi.org/10.1007/s13042-017-0705-5
H. Liu y L. Wang, “Gesture recognition for human-robot collaboration: A review”, Int. J. Ind. Ergon., vol. 68, pp. 355–367, Nov. 2018. https://doi.org/10.1016/J.ERGON.2017.02.004
H. Cheng, L. Yang, y Z. Liu, “Survey on 3D Hand Gesture Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 9, pp. 1659–1673, Sep. 2016. https://doi.org/10.1109/TCSVT.2015.2469551
D. J. Botina-Monsalve, M. A. Domínguez-Vásquez, C. A. Madrigal-González, y A. E. Castro-Ospina, “Automatic classification of vowels in Colombian sign language”, TecnoLógicas, vol. 21, no. 41, pp. 103–114, Jan. 2018. https://doi.org/10.22430/22565337.730
Kui Liu, C. Chen, R. Jafari, y N. Kehtarnavaz, “Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition,” IEEE Sensors Journal, vol. 14, no. 6, pp. 1898–1903, Jun. 2014. https://doi.org/10.1109/JSEN.2014.2306094
C. Chen, R. Jafari, y N. Kehtarnavaz, “A survey of depth and inertial sensor fusion for human action recognition,” Multimedia Tools and Applications, vol. 76, no. 3, pp. 4405–4425, Feb. 2017. https://doi.org/10.1007/s11042-015-3177-1
R. Xu, S. Zhou, y W. J. Li, “MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition,” IEEE Sensors Journal, vol. 12, no. 5, pp. 1166–1173, May. 2012. https://doi.org/10.1109/JSEN.2011.2166953
M. B. I. Raez, M. S. Hussain, y F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications”, Biological procedures online, vol. 8, pp. 11–35, 2006. https://doi.org/10.1251/bpo115
Y. Du, W. Jin, W. Wei, Y. Hu, y W. Geng, “Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation,” Sensors, vol. 17, no. 3, pp. 458, Feb. 2017. https://doi.org/10.3390/s17030458
P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen, y A. Skodras, “Hand Gesture Recognition Based on EMG Data: A Convolutional Neural Network Approach,” in International Conference on Physiological Computing Systems, Sevilla, 2019, pp. 180-197. https://doi.org/10.1007/978-3-030-27950-9_10
W.-T. Shi, Z.-J. Lyu, S.-T. Tang, T.-L. Chia, y C.-Y. Yang, “A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study,” Biocybernetics and Biomedical Engineering, vol. 38, no. 1, pp. 126–135, Nov. 2018. https://doi.org/10.1016/J.BBE.2017.11.001
A. S. Kundu, O. Mazumder, P. K. Lenka, y S. Bhaumik, “Hand Gesture Recognition Based Omnidirectional Wheelchair Control Using IMU and EMG Sensors,” Journal of Intelligent & Robotic Systems, vol. 91, no. 3–4, pp. 529–541, Sep. 2018. https://doi.org/10.1007/s10846-017-0725-0
S. Benatti et al., “A sub-10mW real-time implementation for EMG hand gesture recognition based on a multi-core biomedical SoC,” in 2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI), Vieste 2017, pp. 139-144. https://doi.org/10.1109/IWASI.2017.7974234
M. Rossi, S. Benatti, E. Farella, y L. Benini, “Hybrid EMG classifier based on HMM and SVM for hand gesture recognition in prosthetics,” en 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, 2015, pp. 1700-1705. https://doi.org/10.1109/ICIT.2015.7125342
A. Rahimi, S. Benatti, P. Kanerva, L. Benini, y J. M. Rabaey, “Hyperdimensional biosignal processing: A case study for EMG-based hand gesture recognition,” en 2016 IEEE International Conference on Rebooting Computing (ICRC), San Diego, CA, 2016, pp. 1-8. https://doi.org/10.1109/ICRC.2016.7738683
J. Nielsen, “Finding usability problems through heuristic evaluation,” en Proceedings of the SIGCHI conference on Human factors in computing systems - CHI ’92, California, 1992, pp. 373–380. https://doi.org/10.1145/142750.142834
G. Naik, D. Kumar y Jayadeva, “Twin SMV for Gesture Classification Using the Surface Electromyogram”, IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 2, pp. 301-308, Mar. 2010. https://doi.org/10.1109/TITB.2009.2037752
F.-S. Chen, C.-M. Fu, y C.-L. Huang, “Hand gesture recognition using a real-time tracking method and hidden Markov models,” Image and Vision Computing, vol. 21, no. 8, pp. 745–758, Aug. 2003. https://doi.org/10.1016/S0262-8856(03)00070-2
I.-J. Ding y Y.-J. Chang, “HMM with improved feature extraction-based feature parameters for identity recognition of gesture command operators by using a sensed Kinect-data stream,” Neurocomputing, vol. 262, pp. 108–119, Nov. 2017. https://doi.org/10.1016/J.NEUCOM.2016.11.089
N. H. Dardas y N. D. Georganas, “Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques,” IEEE Trans. Instrum. Measurement, vol. 60, no. 11, pp. 3592–3607, Nov. 2011. https://doi.org/10.1109/TIM.2011.2161140
N. Dardas, Q. Chen, N. D. Georganas, y E. M. Petriu, “Hand gesture recognition using Bag-of-features and multi-class Support Vector Machine,” en 2010 IEEE International Symposium on Haptic Audio Visual Environments and Games, Phoenix, 2010, pp. 1–5. https://doi.org/10.1109/HAVE.2010.5623982
M. A. Oskoei y H. Hu, “Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 8, pp. 1956–1965, Aug. 2008. https://doi.org/10.1109/TBME.2008.919734
M. Leon, J. M. Gutierrez, L. Leija, y R. Munoz, “EMG pattern recognition using Support Vector Machines classifier for myoelectric control purposes,” en 2011 Pan American Health Care Exchanges, Rio de Janeiro, 2011, pp. 175–178. https://doi.org/10.1109/PAHCE.2011.5871873
N. Wang, Y. Chen, y X. Zhang, “The recognition of multi-finger prehensile postures using LDA,” Biomedical Signal Processing and Control, vol. 8, no. 6, pp. 706–712, Nov. 2013. https://doi.org/10.1016/j.bspc.2013.06.006
G. D. F. Ulloa, N. Sreenivasan, P. Bifulco, M. Cesarelli, G. Gargiulo, y U. Gunawardana, “Cost effective electro — Resistive band based myo activated prosthetic upper limb for amputees in the developing world,” en 2017 IEEE Life Sciences Conference (LSC), Sydney, 2017, pp. 250–253. https://doi.org/10.1109/LSC.2017.8268190
C. Savur y F. Sahin, “American Sign Language Recognition system by using surface EMG signal,” 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, 2016, pp.1-6. https://doi.org/10.1109/SMC.2016.7844675
L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257–286, Feb. 1989. https://doi.org/10.1109/5.18626
A. Cohen, “Hidden Markov models in biomedical signal processing,” en Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286), Hong Kong, 1998, pp. 1145-1150. https://doi.org/10.1109/IEMBS.1998.747073
D. Burton, J. Shore, y J. Buck, “A generalization of isolated word recognition using vector quantization,” ICASSP '83. IEEE International Conference on Acoustics, Speech, and Signal Processing, Boston, 1983, pp. 1021–1024. https://doi.org/10.1109/ICASSP.1983.1171915
E. Nowak, F. Jurie, y B. Triggs, “Sampling Strategies for Bag-of-Features Image Classification,” en European Conference on Computer Vision– ECCV 2006, Berlin-Heidelberg, 2006, pp. 490–503. https://doi.org/10.1007/11744085_38
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