Human Activities Recognition using Semi-Supervised SVM and Hidden Markov Models
| dc.creator | Morales García , Santiago | |
| dc.creator | Henao Baena , Carlos | |
| dc.creator | Calvo Salcedo, Andrés | |
| dc.date | 2022-12-22 | |
| dc.date.accessioned | 2025-10-01T23:52:51Z | |
| dc.description | Automatic human activity recognition is an area of interest for developing health, security, and sports applications. Currently, it is necessary to develop methods that facilitate the training process and reduce the costs of this process. This paper explores a methodology to classify human physical activities in a semi-supervised paradigm. With this approach, it is possible to reduce the number of labels necessary to train the learning model and the complexity of this process. This process begins by deducting the number of micro-movements or sub-movements where the data should be grouped and assigning the label through a clustering technique. We perform this procedure for a specific group of micro-movements whose label is unknown. Later, the classification process starts by using two methods, a Support Vector Machine (SVM) that identifies the micro-movements and a Markov Hidden Model that detects the human physical activity as a function of sequences. The results show that with a percentage of 80 % of the known labels, we achieved outcomes like the supervised paradigms found in the literature. This facilitates training these learning models by reducing the number of examples requiring labels and reduces the economic costs, which is one of the significant limitations of machine learning processes. | en-US |
| dc.description | El reconocimiento automático de la actividad humana es un área de interés para el desarrollo de aplicaciones en salud, seguridad y deportes. Actualmente, es necesario desarrollar métodos que faciliten el proceso de entrenamiento y reduzcan los costos de este proceso. Este trabajo explora una metodología para clasificar actividades físicas humanas en un paradigma semi-supervisado. Con este enfoque, es posible reducir el número de etiquetas necesarias para entrenar el modelo de aprendizaje y la complejidad de este proceso. Este proceso comienza deduciendo el número de micro-movimientos o submovimientos en los que deben agruparse los datos y asignando la etiqueta mediante una técnica de clustering. Realizamos este procedimiento para un grupo específico de micro-movimientos cuya etiqueta se desconoce. Posteriormente, se inicia el proceso de clasificación utilizando dos métodos, una Máquina de Vectores Soportados (SVM) que identifica los micro-movimientos y un Modelo Oculto de Markov que detecta la actividad física humana en función de secuencias. Los resultados muestran que con un porcentaje del 80 % de las etiquetas conocidas, se consigue resultados como los paradigmas supervisados encontrados en la literatura. Esto facilita el entrenamiento de estos modelos de aprendizaje al reducir el número de ejemplos que requieren etiquetas y reduce los costes económicos, que es una de las limitaciones significativas de los procesos de aprendizaje automático. | es-ES |
| dc.format | application/pdf | |
| dc.format | application/zip | |
| dc.format | text/xml | |
| dc.format | text/html | |
| dc.identifier | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2474 | |
| dc.identifier | 10.22430/22565337.2474 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12622/7854 | |
| dc.language | eng | |
| dc.publisher | Instituto Tecnológico Metropolitano (ITM) | es-ES |
| dc.relation | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2474/2677 | |
| dc.relation | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2474/2694 | |
| dc.relation | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2474/2695 | |
| dc.relation | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2474/2696 | |
| dc.relation | /*ref*/A. F. Calvo, G. A. Holguin, and H. Medeiros, “Human Activity Recognition Using Multi-modal Data Fusion,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Springer International Publishing, 2019, pp. 946–953. https://doi.org/10.1007/978-3-030-13469-3_109 | |
| dc.relation | /*ref*/R. Gravina, P. Alinia, H. Ghasemzadeh, and G. Fortino, “Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges,” Information Fusion, vol. 35, pp. 68–80, May 2017, https://doi.org/10.1016/j.inffus.2016.09.005 | |
| dc.relation | /*ref*/M. Jiang, J. Kong, G. Bebis, and H. Huo, “Informative joints based human action recognition using skeleton contexts,” Signal Process Image Commun, vol. 33, pp. 29–40, Apr. 2015, https://doi.org/10.1016/j.image.2015.02.004 | |
| dc.relation | /*ref*/A. Bhattacharya, A. Sarkar, and P. Basak, “Time domain multi-feature extraction and classification of human hand movements using surface EMG,” in 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Jan. 2017, pp. 1–5. https://doi.org/10.1109/ICACCS.2017.8014594 | |
| dc.relation | /*ref*/A. Bayat, M. Pomplun, and D. A. Tran, “A Study on Human Activity Recognition Using Accelerometer Data from Smartphones,” Procedia Comput Sci, vol. 34, pp. 450–457, Dec. 2014, https://doi.org/10.1016/j.procs.2014.07.009 | |
| dc.relation | /*ref*/M. Bocksch, J. Seitz, and J. Jahn, “Pedestrian Activity Classification to Improve Human Tracking and Localization,”, in 2013 International Conference on Indoor Positioning and Indoor Navigation, Dec. 2013, pp. 667–671. [Online]. Available: https://www.researchgate.net/publication/259885771_Pedestrian_Activity_Classification_to_Improve_Human_Tracking_and_Localization | |
| dc.relation | /*ref*/C. Wu, J. Zhang, S. Savarese, and A. Saxena, “Watch-n-patch: Unsupervised understanding of actions and relations,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2015, pp. 4362–4370. https://doi.org/10.1109/CVPR.2015.7299065 | |
| dc.relation | /*ref*/E. H. Spriggs, F. de La Torre, and M. Hebert, “Temporal segmentation and activity classification from first-person sensing,” in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Jun. 2009, pp. 17–24. https://doi.org/10.1109/CVPRW.2009.5204354 | |
| dc.relation | /*ref*/F. Destelle et al., “Low-cost accurate skeleton tracking based on fusion of kinect and wearable inertial sensors,” in 2014 22nd European Signal Processing Conference (EUSIPCO), Sep. 2014, pp. 371–375. Accessed: Oct. 31, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6952093 | |
| dc.relation | /*ref*/D. Martín de Castro, “Aplicación Android para el reconocimiento automático de actividades físicas en tiempo real,” Universidad Carlos III de Madrid., Madrid, España, 2012. Accessed: Nov. 14, 2021. [Online]. Available: http://hdl.handle.net/10016/17138 | |
| dc.relation | /*ref*/L. E. Pamplona-Beron, C. A. Henao Baena, and A. F. Calvo-Salcedo, “Human activity recognition using penalized support vector machines and Hidden Markov Models,” Revista Facultad de Ingeniería Universidad de Antioquia, no. 103, pp. 152–163, May 2021, https://doi.org/10.17533/udea.redin.20210532 | |
| dc.relation | /*ref*/M. Georgi, C. Amma, and T. Schultz, “Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing,” in Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, Dec. 2015, pp. 99–108. https://doi.org/10.5220/0005276900990108 | |
| dc.relation | /*ref*/H. Tannous et al., “A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements,” Sensors, vol. 16, no. 11, p. 1914, Nov. 2016, https://doi.org/10.3390/s16111914 | |
| dc.relation | /*ref*/S. Feng and R. Murray-Smith, “Fusing Kinect Sensor and Inertial Sensors with Multi-rate Kalman Filter,” in IET Conference on Data Fusion & Target Tracking 2014: Algorithms and Applications, 2014, pp. 1–8. https://doi.org/10.1049/cp.2014.0527 | |
| dc.relation | /*ref*/S. Gaglio, G. L. Re, and M. Morana, “Human Activity Recognition Process Using 3-D Posture Data,” IEEE Trans Hum Mach Syst, vol. 45, no. 5, pp. 586–597, Oct. 2015, https://doi.org/10.1109/THMS.2014.2377111 | |
| dc.relation | /*ref*/K. Chen, D. Zhang, L. Yao, B. Guo, Z. Yu, and Y. Liu, “Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges, and Opportunities,” ACM Comput Surv, vol. 54, no. 4, pp. 1–40, May 2022, https://doi.org/10.1145/3447744 | |
| dc.relation | /*ref*/R. Mutegeki and D. S. Han, “A CNN-LSTM Approach to Human Activity Recognition,” in 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Feb. 2020, pp. 362–366. https://doi.org/10.1109/ICAIIC48513.2020.9065078 | |
| dc.relation | /*ref*/A. Bevilacqua, K. MacDonald, A. Rangarej, V. Widjaya, B. Caulfield, and T. Kechadi, “Human Activity Recognition with Convolutional Neural Networks,” in Machine Learning and Knowledge Discovery in Databases, Springer International Publishing, 2019, pp. 541–552. https://doi.org/10.1007/978-3-030-10997-4_33 | |
| dc.relation | /*ref*/M. Robnik-Šikonja and I. Kononenko, “Theoretical and Empirical Analysis of ReliefF and RReliefF,” Mach Learn, vol. 53, pp. 23–69, Dec. 2003, [Online]. Available: https://link.springer.com/article/10.1023/A:1025667309714 | |
| dc.relation | /*ref*/J. Shlens, “A Tutorial on Principal Component Analysis,” Apr. 2014, [Online]. Available: http://arxiv.org/abs/1404.1100 | |
| dc.relation | /*ref*/K. Y. Yeung and W. L. Ruzzo, “Principal component analysis for clustering gene expression data,” Bioinformatics, vol. 17, no. 9, pp. 763–774, Sep. 2001, https://doi.org/10.1093/bioinformatics/17.9.763 | |
| dc.relation | /*ref*/P. Cunningham, B. Kathirgamanathan, and S. J. Delany, “Feature Selection Tutorial with Python Examples,” Jun. 2021, [Online]. Available: http://arxiv.org/abs/2106.06437 | |
| dc.relation | /*ref*/L. Zelnik-Manor and P. Perona, “Self-Tuning Spectral Clustering,” in Adv. Neural Inf. Process. Syst, Dec. 2004, vol. 17. [Online]. Available: https://proceedings.neurips.cc/paper/2004/file/40173ea48d9567f1f393b20c855bb40b-Paper.pdf | |
| dc.relation | /*ref*/D. Niu, J. G. Dy, and M. I. Jordan, “Dimensionality Reduction for Spectral Clustering.,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Dec. 2011, vol. 15, pp. 552–560. Accessed: Dec. 06, 2021. [Online]. Available: http://proceedings.mlr.press/v15/niu11a/niu11a.pdf | |
| dc.relation | /*ref*/J. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines,” Dec. 1998, Accessed: Oct. 06, 2021. [Online]. Available: https://www.microsoft.com/en-us/research/publication/sequential-minimal-optimization-a-fast-algorithm-for-training-support-vector-machines/ | |
| dc.relation | /*ref*/A. Rahimi and B. Recht, “Random Features for Large-Scale Kernel Machines,” in Advances in Neural Information Processing Systems, 2007, vol. 20, pp. 1–8. [Online]. Available: https://proceedings.neurips.cc/paper/2007/file/013a006f03dbc5392effeb8f18fda755-Paper.pdf | |
| dc.relation | /*ref*/ | |
| dc.rights | Derechos de autor 2022 TecnoLógicas | es-ES |
| dc.rights | http://creativecommons.org/licenses/by-nc-sa/4.0 | es-ES |
| dc.source | TecnoLógicas; Vol. 26 No. 56 (2023); e2474 | en-US |
| dc.source | TecnoLógicas; Vol. 26 Núm. 56 (2023); e2474 | es-ES |
| dc.source | 2256-5337 | |
| dc.source | 0123-7799 | |
| dc.subject | Spectral clustering | en-US |
| dc.subject | semi-supervised learning | en-US |
| dc.subject | motion estimation | en-US |
| dc.subject | data fusion | en-US |
| dc.subject | human activity recognition | en-US |
| dc.subject | Agrupamiento espectral | es-ES |
| dc.subject | aprendizaje semisupervisado | es-ES |
| dc.subject | estimación de movimiento | es-ES |
| dc.subject | fusión de datos | es-ES |
| dc.subject | reconocimiento de actividad humana | es-ES |
| dc.title | Human Activities Recognition using Semi-Supervised SVM and Hidden Markov Models | en-US |
| dc.title | Reconocimiento de actividades humanas mediante SVM semisupervisado y modelos ocultos de Markov | es-ES |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | Research Papers | en-US |
| dc.type | Artículos de investigación | es-ES |
Archivos
Bloque original
1 - 4 de 4