Sistem Presentasi Cerdas Menggunakan Pengenalan Gerakan Tangan Berdasarkan Klasifikasi Dari Sinyal Electromyography (EMG) Menggunakan Myo Armband
Technological developments to support the current learning system are so fast that there is an interactive innovation technology for educational trends. One of the technologies implemented is an interactive presentation application in a multimedia class or smart presentation system. This technology makes it possible to control the presentation in a natural way with their hand movements. This introduction can replace conventional mouse roles and functions to facilitate teacher performance in applying interactive technology in the classroom. To build this intelligent presentation system, it is divided into several parts: 1) Recognition sensor arm movement using Myo armband; 2) Hand gesture of hand movements made several steps include: a) data retrieval based on realtime and wireless; b) feature extraction; c) classification using artificial neural network; and 3) Smart presentation, is a presentation system that can understand human behavior and provide interactive presentations.The expected benefits of the results of this study are, with the construction of intelligent presentation systems using hand-gesturing recognition based on the classification of electromyography signals, 1) Make presentations more efficient, engaging and easier to understand, and also make the discussion more interactive and improve communication; 2) Assists the presenter of material in exposing the material by using a presentation control system based on hand gestures.
Sukaridhoto, S., Assidiqi, M.H., Salim, N.N.A., 2014. Simple Interactive Projector Based on Hand Gesture Movement. International Electronics Symposium (IES) 2014. ISBN : 978-602-0917-14-6
Wardhany, V.A., kurnia, M.H., Sukaridhoto, S., Sudarsono, A., Pramadihanto, D. 2015. Smart Presentation System using Hand Gestures and Indonesian Speech Command. International Electronics Symposium (IES) 2015 Page:68 – 72
Wardhany, V.A., Sukaridhoto, S., Sudarsono. 2014. Indonesian Automatic Speech Recognition For CommandSpeech Controller Multimedia Player. EMITTER International Journal of Engineering TechnologyVol.2, No.2, December 2014
Hidayat, A.A., Arief, Z., Happyanto, D.C. 2015. LOVETT Scaling with Flex Sensor and MYO Armband for Monitoring Finger Muscles Therapy of Post-Stroke People. International Journal of Engineering Technology Vol.3, No. 2, December 2015
Thalmic lab. MYO armband tech specs, 2016. Gestures and Motionhttps:https://www.myo.com/techspecs
Boyali, A., Hashimoto, N., Matsumoto, O. 2015. Spectral Collaborative Representation Based Classification by Circulants and its Application to Hand Gesture and Posture Recognition from Electromyography Signals. Int'l Conf. IP, Comp. Vision, and Pattern Recognition (IPCV'15)
Nymoen, K., Haugen, M.R., Jensenius, A.R. 2015. MuMYO — Evaluating and Exploring the MYO Armband for Musical Interaction. Proceedings of the International Conference on New Interfaces for Musical Expression, Baton Rouge, LA, USA, May 31-June 3, 2015
Boyali, A., Hashimoto, N., 2016. Spectral Collaborative Representation based Classification for Hand Gestures recognition on Electromyography Signals. Biomedical Signal Processing and Control Volume 24, February 2016, Pages 11–18
Phinyomark, A., Phukpattaranont, P., Limsakul, C. 2011. Electromyography (EMG) Signal Classification Based on Detrended Fluctuation Analysis. Fluctuation and Noise Letters Vol. 10, No. 3 (2011) 281–301
Phinyomark, A., Phukpattaranont, P., Limsakul, C. 2012. Feature reduction and selection for EMG signal classification. Expert Systems with Applications 39 (2012) 7420–7431
Phinyomark, A., Thongpanja, S., Quaine, F., Laurillau, Y., Limsakul, C., Phukpattaranont, P. 2013. Optimal EMG Amplitude Detectors for Muscle-Computer Interface. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2013 10th International Conference on pp.1-6
Rubana, H. C., Mamun B. I., Alauddi, M., Ashrif A. A. B., Kalaivani C., Chang G., 2013. Surface Electromyography Signal Processing and Classification Techniques. Sensors 2013, 13, 12431-12466; doi:10.3390/s130912431
Subasi, Abdulhamit. 2012. Classification of EMG signals using combined features and soft computing techniques. Applied Soft Computing 12 (2012) 2188–2198
Peters, T. 2014. An Assessment of Single-Channel EMG Sensing for Gestural Input. Online: http://www.cs.dartmouth.edu/~traviswp/papers/TR/peters_emg_14.pdf diakses pada tanggal 3 mei 2016
Le, H., Nguyen K.C., Pham, T., Nguyen, V., Tran, M. 2013. Multimodal Smart Interactive Presentation System. Human-Computer Interaction, Part IV, HCII 2013, LNCS 8007, pp. 67–76, 2013. Springer-Verlag Berlin Heidelberg
Butnariu, S., Girbacia, F. 2012. Development Of A Natural User Interface For Intuitive Presentations In Educational Process. The 8th International Scientific Conference eLearning and software for Education, Bucharest, April 26-27, 2012
Authors who publish with this journal agree to the following terms:
1. Copyright on any article is retained by the author(s).
2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
5. The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License