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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| A drastic increase in healthcare demand has come from the explosive growth of the older population. The elderly are, on average, more vulnerable to health problems than other age groups. Unpredictable events, such as sudden falls, can be avoided with proper monitoring. Activity recognition can help people avoid potentially dangerous behaviours by aiding in the detection of unexpected events. Most of the existing approaches require complex sensors and environment setup, involve data filtering and noise removal steps, and most often the chosen learning models need to be tuned and carefully designed for optimal performance. This study emphasizes light implementation, fast training time, easy experimental setup, and minimal parameter tuning. Human activities are captured using smartphone sensors in this study. Students and senior residents from a local home care facility are among the volunteers for this study. The necessary data sets are obtained from the accelerometer sensor on the smartphone. To provide baseline performance, the traditional instant-based learning architectures k-Nearest Neighbors (kNN) and Support Vector Machine (SVM) are used. To represent the ensemble learning model, the Random Forest (RF) and XGBoost (XGB) are investigated. The Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) are the more advanced deep learning models used in this study (CNN). The results reveal that ensemble methods and deep learning models provide improved accuracy, with ensemble learning models outperforming deep learning models. |
| References |
Agarwal, P., & Alam, M. (2022). Quantum-Inspired Support Vector Machines for Human Activity Recognition in Industry 4.0. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 90. Springer, Singapore. Alsheikh, M. A., Selim, A., Niyato, D., Doyle, L., Lin, S., & Tan, H. P. (2016). Deep activity recognition models with triaxial accelerometers. In Workshops at the Thirtieth AAAI Conference on Artificial Intelligence. Ayumi, V. (2016). Pose-based human action recognition with Extreme Gradient Boosting. Proceedings of the 2016 IEEE Student Conference on Research and Development (SCOReD Birant, D., & Yalniz, K. (2022). Animal activity recognition from sensor data using ensemble learning. emerging trends in iot and integration with data science, cloud computing, and big data Analytics, IGI-Global, 165-180. Boga, J. (2022). Human activity recognition in WBAN using ensemble model. International Journal of Pervasive Computing and Communications, 1-5. Catal, C., Tufekci, S., Pirmit, E., & Kocabag, G. (2015). On the use of ensemble of classifiers for accelerometer-based activity recognition. Applied Soft Computing, 37, 1018-1022. Chathuramali, K. G. M., & Rodrigo, R. (2021). Faster human activity recognition with SVM. Proceedings of the International Conference on Advances in ICT for Emerging Regions (ICTer2012), pp. 197-203. Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S. T., Tröster, G., Millán, J. R., & Roggen, D. (2013). The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recognition Letters, 34(15), 2033-2042. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, Choudhury, N. A., Moulik, S., & Roy, D. S. (2021). Physique-based Human Activity Recognition using Ensemble Learning and Smartphone Sensors. IEEE Sensors Journal, 21(15), 16852-16860. Cook, D., Feuz, K. D., & Krishnan, N. C. (2013). Transfer learning for activity recognition: a survey. Knowledge and Information Systems, 36, 537–556. Gaur, D., & Dubey, S. K. (2022). Human Activities Analysis Using Machine Learning Approaches. In: Dua, M., Jain, A.K., Yadav, A., Kumar, N., Siarry, P. (eds) Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. Gorjani, O. M., Byrtus, R., Dohnal, J., Bilik, P., Koziorek, J., & Martinek, R. (2021). Human Activity Classification Using Multilayer Perceptron. Sensors, 21(18), 6207. Hammerla, N. Y., Halloran, S., & Plötz, T. (2016). Deep, convolutional, and recurrent models for human activity recognition using wearables. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 1533-1540. Ignatov, A. D., & Strijov V. V. (2016). Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer. Multimedia Tools And Applications, 75(12), 7257-7270. Khan, I. U., Afzal, S., & Lee, J. W. (2022). Human Activity Recognition via Hybrid Deep Learning Based Model. Sensors, 22(1), 323. Kim, Y., & Toomajian, B. (2016). Hand Gesture Recognition Using Micro-Doppler Signatures with Convolutional Neural Network. IEEE Access, 4, 7125-7130. Kumar, P., Chuke, D. L., Bhatia, P. K., & Mehrotra, D. (2021). Assessing the Supervised Machine Learning Algorithms for Human Activity Recognition Using Smartphone. Soft Computing: Theories and Applications, 329-338. Li, X., He, Y., Fioranelli, F., & Jing, X. (2021). Semisupervised Human Activity Recognition with Radar Micro-Doppler Signatures. IEEE Transactions on Geoscience and Remote Sensing. Manjarrés, J., Lan, G., Gorlatova, M., Hassan, M., & Pardo, M. (2021). Enhancing Kinetic Energy Harvesting-based Human Activity Recognition with Deep Learning and Data Augmentation. IEEE Internet of Things Journal, 9(10), 7545-7558. Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., & de Mendonça, A. (2011). Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Research Notes, 4, 299. D Mohsen, S., Elkaseer, A., & Scholz, S. G. (2022). Human Activity Recognition Using K-Nearest Neighbor Machine Learning Algorithm. In: Scholz, S.G., Howlett, R.J., Setchi, R. (eds) Sustainable Design and Manufacturing. KES-SDM 2021. Smart Innovation, Systems and Technologies, Vol 262. Springer. Nurwulan, N. R., & Selamaj, G. (2020) Random Forest for Human Daily Activity Recognition. Journal of Physics: Conference Series, Vol. 1655, Universitas Riau International Conference on Science and Environment 2020 (URICSE-2020) 11-13 September 2020, Pekanbaru, Riau, Indonesia. Porwal, K., Gupta, R., Naik, T. G., & Vijayarajan, V. (2020). Recognition of Human Activities in a Controlled Environment using CNN. Proc. of the 2020 International Conference on Smart Electronics and Communication (ICOSEC), pp. 291-296. Qin, J., Liu, L., Zhang, Z., Wang, Y., & Shao, L. (2016). Compressive Sequential Learning for Action Similarity Labeling. IEEE Transactions on Image Processing, 25(2), 756-769. Ravi, D., Wong, C., Lo, B., & Yang, G. Z. (2016). A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE Journal of Biomedical and Health Informatics, 21(1), 56-64. Roche, J., De-Silva, V., Hook, J., Moencks, M., & Kondoz, A. (2021). A Multimodal Data Processing System for LiDAR-Based Human Activity Recognition. IEEE Transactions on Cybernetics, 1-5. Journal of Science and Mathematics Letters, Vol 10, Issue 1, 2022 (32-43) ISSN 2462-2052, eISSN 2600-8718 Ronao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235-244. Smith, S. (2016, February 11). 10 Common Elderly Health Issues. Retrieved from https://vitalrecord.tamhsc.edu/10-common-elderly-health-issues/ Tan, T. H., Wu, J. Y., Liu, S. H., & Gochoo, M. (2022). Human Activity Recognition Using an Ensemble Learning Algorithm with Smartphone Sensor Data. Electronics, 11(3), 322. Vepakomma, P., De, D., Das, S. K., & Bhansali, S. (2015). A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities. Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 1-6. Walse, K. H, Dharaskar R. V., & Thakare V. M. (2016). A study of human activity recognition using AdaBoost classifiers on WISDM dataset. The Institute of Integrative Omics and Applied Biotechnology Journal, 7(2), 68-76. Wang J., Chen Y., Hao S., Peng X., & Hu L. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters, 19, 3-11. Yang, J., Nguyen, M. N., San, P. P., Li, X. L., & Krishnaswamy, S. (2015). Deep convolutional neural networks on multichannel time series for human activity recognition. Proc. of the 24th Int. Joint Conf. on Artificial Intelligence. Zeng, M., Nguyen, L. T., Yu, B., Mengshoel, O. J., Zhu, J., Wu, P., & Zhang, J. (2014). Convolutional neural networks for human activity recognition using mobile sensors. Proc. of the IEEE 6th Int. Conf. on Mobile Computing, Applications and Services, 197-205. Zul, M. I., Muslim, I. & Hakim, L. (2017). Human Activity Recognition by Using Nearest Neighbor Algorithm from Digital Image. Proceedings of the 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT), pp. 58-61.
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