Connecting reality and virtuality with Human Machine Interface (HMI)
This thesis explores the development and application of machine learning models to predict hand gestures using electromyography (EMG) data. The primary focus is on leveraging a convolutional neural network (CNN) for detecting and analyzing hand gestures based on electrical signals from muscle contra...
- Autores:
-
Bastidas Peralta, Luciano
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/74984
- Acceso en línea:
- https://hdl.handle.net/1992/74984
- Palabra clave:
- HMI
EMG
Human machine interface
Electromyography
Machine learning
Deep Learning
Electrodes
Ingeniería
- Rights
- openAccess
- License
- Attribution 4.0 International
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Connecting reality and virtuality with Human Machine Interface (HMI) |
| title |
Connecting reality and virtuality with Human Machine Interface (HMI) |
| spellingShingle |
Connecting reality and virtuality with Human Machine Interface (HMI) HMI EMG Human machine interface Electromyography Machine learning Deep Learning Electrodes Ingeniería |
| title_short |
Connecting reality and virtuality with Human Machine Interface (HMI) |
| title_full |
Connecting reality and virtuality with Human Machine Interface (HMI) |
| title_fullStr |
Connecting reality and virtuality with Human Machine Interface (HMI) |
| title_full_unstemmed |
Connecting reality and virtuality with Human Machine Interface (HMI) |
| title_sort |
Connecting reality and virtuality with Human Machine Interface (HMI) |
| dc.creator.fl_str_mv |
Bastidas Peralta, Luciano |
| dc.contributor.advisor.none.fl_str_mv |
Camargo Leyva, Jonathan |
| dc.contributor.author.none.fl_str_mv |
Bastidas Peralta, Luciano |
| dc.subject.keyword.none.fl_str_mv |
HMI EMG |
| topic |
HMI EMG Human machine interface Electromyography Machine learning Deep Learning Electrodes Ingeniería |
| dc.subject.keyword.eng.fl_str_mv |
Human machine interface Electromyography Machine learning Deep Learning Electrodes |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
This thesis explores the development and application of machine learning models to predict hand gestures using electromyography (EMG) data. The primary focus is on leveraging a convolutional neural network (CNN) for detecting and analyzing hand gestures based on electrical signals from muscle contractions. Data acquisition is facilitated through a Cyton OpenBCI board, with electrodes placed on the forearm. Initial challenges with electrode placement using kinesiology tape were addressed by designing a 3D-printed armband to ensure secure and consistent electrode po sitioning. Various machine learning models, including simple and slightly larger CNNs, as well as a pre-trained 3DResNet, were tested for their accuracy and training efficiency. The study reveals that while the models can achieve reasonable accuracy, incor porating additional parameters such as angular velocity of finger joints significantly complicates the training process. A new graphical user interface (GUI) was devel oped to distinguish between dynamic and static gestures, enhancing real-time applicability. The results indicate that although current models can be integrated into real life scenarios, further improvements are needed for effective real-time prediction, particularly for gestures involving thumb movements. Future work will focus on refining the model and incorporating more sophisticated data processing techniques to improve accuracy and usability in practical applications. |
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2024 |
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2024-08-05T16:06:09Z |
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2024-08-02 |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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eng |
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S. M. A. Iqbal, I. Mahgoub, E Du, M. A. Leavitt, and W. Asghar, “Advances in healthcare wearable devices,” en, npj Flexible Electronics, vol. 5, no. 1, p. 9, Apr. 2021, ISSN: 2397-4621. DOI: 10 . 1038 / s41528 - 021 - 00107 - x. [Online]. Available: https://www.nature.com/articles/s41528-021-00107-x (visited on 07/02/2024). M. R. Ahsan, M. I. Ibrahimy, and O. O. Khalifa, “Electromygraphy (EMG) sig nal based hand gesture recognition using artificial neural network (ANN),” in 2011 4th International Conference on Mechatronics (ICOM), Kuala Lumpur, Malaysia: IEEE, May 2011, pp.1–6, ISBN: 978-1-61284-435-0. DOI: 10 . 1109 /ICOM . 2011 . 5937135. [Online]. Available: http : / / ieeexplore . ieee . org /document/5937135/ (visited on 07/02/2024). P. P. Shinde and S. Shah, “A Review of Machine Learning and Deep Learning Applications,” in 2018 Fourth International Conference on Computing Communi cation Control and Automation (ICCUBEA), Pune, India: IEEE, Aug. 2018, pp. 1–6, ISBN: 978-1-5386-5257-2. DOI: 10 .1109/ ICCUBEA . 2018. 8697857. [Online]. Available: https://ieeexplore.ieee.org/document/8697857/ (visited on 07/02/2024). C. H. Wolters, L Grasedyck, and W Hackbusch, “Efficient computation of lead field bases and influence matrix for the FEM-based EEG and MEG inverse problem,” Inverse Problems, vol. 20, no. 4, pp. 1099–1116, Aug. 2004, ISSN: 0266-5611, 1361-6420. DOI: 10 . 1088 / 0266 - 5611 / 20 / 4 / 007. [Online]. Available: https : / / iopscience . iop . org / article /10 . 1088 / 0266 - 5611 / 20 / 4 / 007(visited on 06/28/2024). D. Farina and A. Holobar, “Characterization of Human Motor Units From Sur face EMG Decomposition,” Proceedings of the IEEE, vol. 104, no. 2, pp. 353–373, Feb. 2016, ISSN: 0018-9219, 1558-2256. DOI: 10 . 1109 / JPROC . 2015 . 2498665. [Online]. Available: http://ieeexplore.ieee.org/document/7386798/ (vis ited on 06/20/2024). M. B. Bromberg, “The motor unit and quantitative electromyography,” en, Muscle & Nerve, vol. 61, no. 2, pp. 131–142, Feb. 2020, ISSN: 0148-639X, 1097-4598. DOI: 10.1002/mus.26718. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1002/mus.26718 (visited on 06/20/2024). N. M. Kakoty, M. Kaiborta, and S. M. Hazarika, “Electromyographic Grasp Recognition for a Five Fingered Robotic Hand,” IAES International Journal of Robotics and Automation (IJRA), vol. 2, no. 1, p. 1, Sep. 2012, ISSN: 2089-4856, 2089-4856. DOI: 10 . 11591 / ijra . v2i1 . pp1 - 10. [Online]. Available: http :/ / ijra . iaescore . com / index . php / IJRA / article / view / 908 (visited on 06/25/2024). B. Belay, B. Malengier, K. Timothy, W. Sitek, J. Krishnamoorthy, and L. Van Langenhove, “A review on the recent developments in design and integra tion of electromyography textile electrodes for biosignal monitoring,” Journal of Industrial Textiles, vol. 53, p. 152 808 372 311 750, May 2023. DOI: 10.1177/15280837231175062. Steven Telleen, Human Anatomy, en. OpenStax CNX. [Online]. Available: http://archive.org/details/cnx-org-col11941 (visited on 07/02/2024). superuser, AI in Healthcare & Medical Affairs, en-US, Jul. 2023. [Online]. Avail able: https : / / bluematterconsulting . com /artificial - intelligence - healthcare-medical-affairs/ (visited on 08/02/2024). P. Bhasha, T. Kumar, and K. Baseer, “Smarteye: A navigation and obstacle de tection for visually impaired people through smart app,” Journal of Applied Engineering and Technological Science (JAETS), vol. 4, pp. 992–1011, Jun. 2023. DOI: 10.37385/jaets.v4i2.2013. Image Recognition Tasks, en. [Online]. Available: https://fastercapital.com/keyword/image-recognition-tasks.html (visited on 08/02/2024). R. Cau, F. Pisu, J. Suri, et al., “Artificial intelligence in the differential diagnosis of cardiomyopathy phenotypes,” Diagnostics, vol. 14, p. 156, Jan. 2024. DOI:10.3390/diagnostics14020156. Cyton Specs | OpenBCI Documentation. [Online]. Available: https : / / docs .openbci.com/Cyton/CytonSpecs/ (visited on 06/29/2024) S. D. M. Vaseem, S. M. Yaseen, S. K. Basha, and S. M. Yousuf, “Identification of Power Quality Disturbances in Electrical Systems - A Signal Processing Approach,” International Journal for Research in Applied Science and Engineer ing Technology, vol. 11, no. 5, pp. 1559–1567, May 2023, ISSN: 23219653. DOI:10.22214/ijraset.2023.51823. [Online]. Available: https://www.ijraset.com/best-journal/identification-of-power-quality-disturbances-in electrical-systems-a-signal-processing-approach (visited on 08/02/2024). S. Sadeqi, N. Xiros, S. Rouhi, J. Ioup, J. VanZwieten, and C. Sultan, “WAVELET TRANSFORMATION ANALYSIS APPLIED TO INCOMPRESSIBLE FLOW FIELD ABOUT A SOLID CYLINDER,” en, in Proceeding of 5-6th Thermal and Fluids Engineering Conference (TFEC), Virtual: Begellhouse, 2021, pp. 353–363. DOI:10.1615/TFEC2021.cmd.036526. [Online]. Available: https://dl.astfe.org/conferences/tfec2021,5658947e4946fa79,49dbd63e41447bc7.html (visited on 08/02/2024). S. Li, C. Jiang, Y. Ma, and C. Li, “Spatiotemporal analysis of hydrometeorolog ical factors in the source region of the dongting lake basin, china,” Atmosphere, vol. 14, no. 12, 2023, ISSN: 2073-4433. DOI: 10.3390/atmos14121793. [Online]. Available: https://www.mdpi.com/2073-4433/14/12/1793. Hand landmarks detection guide | Google AI Edge, en. [Online]. Available:https://ai.google.dev/edge/mediapipe/solutions/vision/hand_landmarker(visited on 08/01/2024). A. I. Anak, Bedanya 1d, 2d, 3d convolution, May 2021. [Online]. Available: https://www.youtube.com/watch?v=MdNMzJ1qyOI |
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Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autoresAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Camargo Leyva, Jonathanvirtual::19711-1Bastidas Peralta, Luciano2024-08-05T16:06:09Z2024-08-05T16:06:09Z2024-08-02https://hdl.handle.net/1992/74984instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/This thesis explores the development and application of machine learning models to predict hand gestures using electromyography (EMG) data. The primary focus is on leveraging a convolutional neural network (CNN) for detecting and analyzing hand gestures based on electrical signals from muscle contractions. Data acquisition is facilitated through a Cyton OpenBCI board, with electrodes placed on the forearm. Initial challenges with electrode placement using kinesiology tape were addressed by designing a 3D-printed armband to ensure secure and consistent electrode po sitioning. Various machine learning models, including simple and slightly larger CNNs, as well as a pre-trained 3DResNet, were tested for their accuracy and training efficiency. The study reveals that while the models can achieve reasonable accuracy, incor porating additional parameters such as angular velocity of finger joints significantly complicates the training process. A new graphical user interface (GUI) was devel oped to distinguish between dynamic and static gestures, enhancing real-time applicability. The results indicate that although current models can be integrated into real life scenarios, further improvements are needed for effective real-time prediction, particularly for gestures involving thumb movements. Future work will focus on refining the model and incorporating more sophisticated data processing techniques to improve accuracy and usability in practical applications.PregradoEquipos de conección humano maquina o wearable technology54 páginasapplication/pdfengUniversidad de los AndesIngeniería MecánicaFacultad de IngenieríaDepartamento de Ingeniería MecánicaConnecting reality and virtuality with Human Machine Interface (HMI)Trabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPHMIEMGHuman machine interfaceElectromyographyMachine learningDeep LearningElectrodesIngenieríaS. M. A. Iqbal, I. Mahgoub, E Du, M. A. Leavitt, and W. Asghar, “Advances in healthcare wearable devices,” en, npj Flexible Electronics, vol. 5, no. 1, p. 9, Apr. 2021, ISSN: 2397-4621. DOI: 10 . 1038 / s41528 - 021 - 00107 - x. [Online]. Available: https://www.nature.com/articles/s41528-021-00107-x (visited on 07/02/2024).M. R. Ahsan, M. I. Ibrahimy, and O. O. Khalifa, “Electromygraphy (EMG) sig nal based hand gesture recognition using artificial neural network (ANN),” in 2011 4th International Conference on Mechatronics (ICOM), Kuala Lumpur, Malaysia: IEEE, May 2011, pp.1–6, ISBN: 978-1-61284-435-0. DOI: 10 . 1109 /ICOM . 2011 . 5937135. [Online]. Available: http : / / ieeexplore . ieee . org /document/5937135/ (visited on 07/02/2024).P. P. Shinde and S. Shah, “A Review of Machine Learning and Deep Learning Applications,” in 2018 Fourth International Conference on Computing Communi cation Control and Automation (ICCUBEA), Pune, India: IEEE, Aug. 2018, pp. 1–6, ISBN: 978-1-5386-5257-2. DOI: 10 .1109/ ICCUBEA . 2018. 8697857. [Online]. Available: https://ieeexplore.ieee.org/document/8697857/ (visited on 07/02/2024).C. H. Wolters, L Grasedyck, and W Hackbusch, “Efficient computation of lead field bases and influence matrix for the FEM-based EEG and MEG inverse problem,” Inverse Problems, vol. 20, no. 4, pp. 1099–1116, Aug. 2004, ISSN: 0266-5611, 1361-6420. DOI: 10 . 1088 / 0266 - 5611 / 20 / 4 / 007. [Online]. Available: https : / / iopscience . iop . org / article /10 . 1088 / 0266 - 5611 / 20 / 4 / 007(visited on 06/28/2024).D. Farina and A. Holobar, “Characterization of Human Motor Units From Sur face EMG Decomposition,” Proceedings of the IEEE, vol. 104, no. 2, pp. 353–373, Feb. 2016, ISSN: 0018-9219, 1558-2256. DOI: 10 . 1109 / JPROC . 2015 . 2498665. [Online]. Available: http://ieeexplore.ieee.org/document/7386798/ (vis ited on 06/20/2024).M. B. Bromberg, “The motor unit and quantitative electromyography,” en, Muscle & Nerve, vol. 61, no. 2, pp. 131–142, Feb. 2020, ISSN: 0148-639X, 1097-4598. DOI: 10.1002/mus.26718. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1002/mus.26718 (visited on 06/20/2024).N. M. Kakoty, M. Kaiborta, and S. M. Hazarika, “Electromyographic Grasp Recognition for a Five Fingered Robotic Hand,” IAES International Journal of Robotics and Automation (IJRA), vol. 2, no. 1, p. 1, Sep. 2012, ISSN: 2089-4856, 2089-4856. DOI: 10 . 11591 / ijra . v2i1 . pp1 - 10. [Online]. Available: http :/ / ijra . iaescore . com / index . php / IJRA / article / view / 908 (visited on 06/25/2024).B. Belay, B. Malengier, K. Timothy, W. Sitek, J. Krishnamoorthy, and L. Van Langenhove, “A review on the recent developments in design and integra tion of electromyography textile electrodes for biosignal monitoring,” Journal of Industrial Textiles, vol. 53, p. 152 808 372 311 750, May 2023. DOI: 10.1177/15280837231175062.Steven Telleen, Human Anatomy, en. OpenStax CNX. [Online]. Available: http://archive.org/details/cnx-org-col11941 (visited on 07/02/2024).superuser, AI in Healthcare & Medical Affairs, en-US, Jul. 2023. [Online]. Avail able: https : / / bluematterconsulting . com /artificial - intelligence - healthcare-medical-affairs/ (visited on 08/02/2024).P. Bhasha, T. Kumar, and K. Baseer, “Smarteye: A navigation and obstacle de tection for visually impaired people through smart app,” Journal of Applied Engineering and Technological Science (JAETS), vol. 4, pp. 992–1011, Jun. 2023. DOI: 10.37385/jaets.v4i2.2013.Image Recognition Tasks, en. [Online]. Available: https://fastercapital.com/keyword/image-recognition-tasks.html (visited on 08/02/2024).R. Cau, F. Pisu, J. Suri, et al., “Artificial intelligence in the differential diagnosis of cardiomyopathy phenotypes,” Diagnostics, vol. 14, p. 156, Jan. 2024. DOI:10.3390/diagnostics14020156.Cyton Specs | OpenBCI Documentation. [Online]. Available: https : / / docs .openbci.com/Cyton/CytonSpecs/ (visited on 06/29/2024)S. D. M. Vaseem, S. M. Yaseen, S. K. Basha, and S. M. Yousuf, “Identification of Power Quality Disturbances in Electrical Systems - A Signal Processing Approach,” International Journal for Research in Applied Science and Engineer ing Technology, vol. 11, no. 5, pp. 1559–1567, May 2023, ISSN: 23219653. DOI:10.22214/ijraset.2023.51823. [Online]. Available: https://www.ijraset.com/best-journal/identification-of-power-quality-disturbances-in electrical-systems-a-signal-processing-approach (visited on 08/02/2024).S. Sadeqi, N. Xiros, S. Rouhi, J. Ioup, J. VanZwieten, and C. Sultan, “WAVELET TRANSFORMATION ANALYSIS APPLIED TO INCOMPRESSIBLE FLOW FIELD ABOUT A SOLID CYLINDER,” en, in Proceeding of 5-6th Thermal and Fluids Engineering Conference (TFEC), Virtual: Begellhouse, 2021, pp. 353–363. DOI:10.1615/TFEC2021.cmd.036526. [Online]. Available: https://dl.astfe.org/conferences/tfec2021,5658947e4946fa79,49dbd63e41447bc7.html (visited on 08/02/2024).S. Li, C. Jiang, Y. Ma, and C. Li, “Spatiotemporal analysis of hydrometeorolog ical factors in the source region of the dongting lake basin, china,” Atmosphere, vol. 14, no. 12, 2023, ISSN: 2073-4433. DOI: 10.3390/atmos14121793. [Online]. Available: https://www.mdpi.com/2073-4433/14/12/1793.Hand landmarks detection guide | Google AI Edge, en. [Online]. Available:https://ai.google.dev/edge/mediapipe/solutions/vision/hand_landmarker(visited on 08/01/2024).A. I. Anak, Bedanya 1d, 2d, 3d convolution, May 2021. [Online]. 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