[1] F. Lanza, V. Seidita, and A. Chella, “Agents and robots for collaborating and supporting physicians in healthcare scenarios,” Journal of Biomedical Informatics, vol. 108, no. January, p. 103483, 2020. [Online]. Available: https://doi.org/10.1016/j.jbi.2020.103483

[2] M. M. Rodgers, V. M. Pai, and R. S. Conroy, “Recent advances in wearable sensors for health monitoring,” IEEE Sensors Journal, vol. 15, no. 6, pp. 3119–3126, 2015.

[3] N. C. Minh, T. H. Dao, N. Q. Huy, D. N. Tran, N. T. Thu, and D. T. Tran, “Evaluation of Smartphone and Smartwatch Accelerometer Data in Activity Classifica-tion,” in 2021 8th NAFOSTED Conference on Information and Computer Science. IEEE, 2021, pp. 33–38.

[4] L. Mo, F. Li, Y. Zhu, and A. Huang, “Human physical activity recognition based on computer vision with deep learning model,” Conference Record - IEEE Instrumentation and Measurement Technology Conference, vol. 2016-July, 2016.

[5] N. Zhu, J. Cao, K. Shen, X. Chen, and S. Zhu, “A deci-sion support system with intelligent recommendation for multi-disciplinary medical treatment,” ACM Trans-actions on Multimedia Computing, Communications and Applications, vol. 16, no. 1s, pp. 1–23, 2020.

[6] S. Chandra Mukhopadhyay, “Wearable Sensors for Human Activity Monitoring: A Review,” IEEE Sensors Journal, vol. 15, no. 3, pp. 1321–1330, 2015.

[7] T. H. Dao, V. C. Ngo, Q. H. Nguyen, D. N. Tran, and D. T. Tran, “Building Human Activity Recognition System using Accelerometers and Machine Learning Methods on Low Performance Microcontrollers,” Research and Devel-opment on Information and Communication Technology, vol. 12/2021, no. 2, pp. 69–76, 2021.

[8] G. Biagetti, P. Crippa, L. Falaschetti, S. Orcioni, and C. Turchetti, “Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes,” BioMedical Engineering Online, vol. 17, no. S1, pp. 1–18, 2018. [Online]. Available: https://doi.org/10.1186/s12938-018-0567-4

[9] S. Chung, J. Lim, K. J. Noh, G. Kim, and H. Jeong, “Sensor data acquisition and multimodal sensor fusion for human activity recognition using deep learning,” in Sensors (Switzerland), vol. 19, no. 7, 2019.

[10] G. Şengül, M. Karakaya, S. Misra, O. O. Abayomi-Alli, and R. Damaševičius, “Deep learning based fall detec-tion using smartwatches for healthcare applications,” Biomedical Signal Processing and Control, vol. 71, no. October 2021, p. 103242, 2022.

[11] Y. Zhao, R. Yang, G. Chevalier, X. Xu, and Z. Zhang, “Deep Residual Bidir-LSTM for Human Activity Recog-nition Using Wearable Sensors,” Mathematical Problems in Engineering, vol. 2018, 2018.

[12] N. T. Thu, T.-h. Dao, B. Q. Bao, D.-n. Tran, P. V. Thanh, and D.-T. Tran, “Real-Time Wearable-Device Based Activity recognition Using Machine Learning Methods,” International Journal of Computing and Digital Systems, vol. 12, no. 1, pp. 321–333, 2022. [Online]. Available: https://dx.doi.org/10.12785/ijcds/120126

[13] D. N. Tran, T. N. Nguyen, P. C. P. Khanh, and D. T. Trana, “An IoT-based Design Using Accelerometers in Animal Behavior Recognition Systems,” IEEE Sensors Journal, vol. 12, no. 18, pp. 17 515–17 528, 2021.

[14] P. C. P. Khanh, D.-T. Tran, V. T. Duong, N. H. Thinh, and D.-N. Tran, “The new design of cows’ behavior classifier based on acceleration data and proposed feature set,” Mathematical Biosciences and Engineering, vol. 17, no. 4, pp. 2760–2780, 2020. [Online]. Available: https://www. aimspress.com/article/doi/10.3934/mbe.2020151

[15] V. Bianchi, M. Bassoli, G. Lombardo, P. Fornacciari, M. Mordonini, and I. De Munari, “IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8553–8562, 2019.

[16] N. Damodaran, E. Haruni, M. Kokhkharova, and J. Schäfer, “Device free human activity and fall recognition using WiFi channel state information (CSI),” CCF Transactions on Pervasive Computing and Interaction, vol. 2, no. 1, pp. 1–17, 2020. [Online]. Available: https://doi.org/10.1007/s42486-020-00027-1

[17] P. Kumar and S. Chauhan, “RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities,” CCF Transactions on Pervasive Computing and Interaction, vol. 3, no. 3, p. 339, 2021. [Online]. Available: https: //doi.org/10.1007/s42486-021-00063-5

[18] J. Qi, P. Yang, M. Hanneghan, S. Tang, and B. Zhou, “A hybrid hierarchical framework for gym physical activity recognition and measurement using wearable sensors,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1384–1393, 2019.

[19] P. Casale, O. Pujol, and P. Radeva, “Human activity recognition from accelerometer data using a wearable device,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6669 LNCS, 2011, pp. 289–296.

[20] M. Milenkoski, K. Trivodaliev, S. Kalajdziski, M. Jovanov, and B. R. Stojkoska, “Real time human activity recogni-tion on smartphones using LSTM networks,” 2018 41st International Convention on Information and Communica-tion Technology, Electronics and Microelectronics, MIPRO 2018 - Proceedings, pp. 1126–1131, 2018.

[21] P. Van Thanh, D. T. Tran, D. C. Nguyen, N. Duc Anh, D. Nhu Dinh, S. El-Rabaie, and K. Sandrasegaran, “Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers,” Arabian Journal for Science and Engineering, vol. 44, no. 4, pp. 3329–3342, 2019. [Online]. Available: https://doi.org/10.1007/s13369-018-3496-4

[22] J. Suto, S. Oniga, C. Lung, and I. Orha, “Comparison of offline and real-time human activity recognition results using machine learning techniques,” Neural Computing and Applications, vol. 32, no. 20, pp. 15 673–15 686, 2020. [Online]. Available: https: //doi.org/10.1007/s00521-018-3437-x

[23] A. T. Özdemir and B. Barshan, “Detecting Falls with Wearable SensorsUsing Machine Learning Techniques,” Sensors, vol. 14, pp. 10 691–10 708, 2014.

[24] T. H. Dao, M. H. Le, D. N. Tran, and D. T. Tran, “Xay dung mang giam sat hanh vi trong toa nha su dung cong nghe wifi,” in REV-ECIT2021. 978-604-80-5958-3, 2021, pp. 48–53.

[25] A. Mannini, S. S. Intille, M. Rosenberger, A. M. Sabatini, and W. Haskell, “Activity recognition using a single accelerometer placed at the wrist or ankle,” Medicine and Science in Sports and Exercise, vol. 45, no. 11, pp. 2193–2203, 2013.

[26] C. Torres-Huitzil and M. Nuno-Maganda, “Robust smartphone-based human activity recognition using a tri-axial accelerometer,” in 2015 IEEE 6th Latin American Symposium on Circuits and Systems, LASCAS 2015 -Conference Proceedings, 2015, pp. 2–5.

[27] D. Rodriguez-Martin, A. Samà, C. Perez-Lopez, A. Català, J. Cabestany, and A. Rodriguez-Molinero, “SVM-based posture identification with a single waist-located triaxial accelerometer,” Expert Systems with Applications, vol. 40, no. 18, pp. 7203–7211, 2013.[Online]. Available: http://dx.doi.org/10.1016/j.eswa. 2013.07.028

[28] D. Naranjo-Hernández, L. M. Roa, J. Reina-Tosina, and M. Á. Estudillo-Valderrama, “SoM: A smart sensor for human activity monitoring and assisted healthy ageing,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 12 PART2, pp. 3177–3184, 2012.

[29] S. Balli, E. A. Sağbaş, and M. Peker, “Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm,” Measurement and Control (United Kingdom), vol. 52, no. 1-2, pp. 37–45, 2019.

[30] R. Caruana and A. Niculescu-Mizil, “An empirical comparison of supervised learning algorithms,” in ACM International Conference Proceeding Series, vol. 148, 2006, pp. 161–168.

[31] A. Mannini and A. M. Sabatini, “Machine learning methods for classifying human physical activity from on-body accelerometers,” Sensors, vol. 10, no. 2, pp. 1154–1175, 2010.

[32] Q. V. Le, “Building high-level features using large scale unsupervised learning,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing -Proceedings, 2013, pp. 8595–8598.

[33] J. Bassen, B. Balaji, M. Schaarschmidt, C. Thille,

[34] J. Painter, D. Zimmaro, A. Games, E. Fast, and J. C. Mitchell, “Reinforcement Learning for the Adaptive Scheduling of Educational Activities,” in Conference on Human Factors in Computing Systems - Proceedings, 2020, pp. 1–12.

[35] D. Guan, W. Yuan, Y. K. Lee, A. Gavrilov, and

[36] S. Lee, “Activity recognition based on semi-supervised learning,” in Proceedings - 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2007, no. 1, 2007, pp. 469–475.

[37] F. Yang and L. Zhang, “Real-time human activity classification by accelerometer embedded wearable devices,” in 2017 4th International Conference on Systems and Informatics, ICSAI 2017, vol. 2018-Janua, no. Icsai, 2017, pp. 469–473.

[38] A. Wang, G. Chen, J. Yang, S. Zhao, and C.-Y. Chang, “A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone,” IEEE Sensors Journal, vol. 16, no. 11, pp. 4566–4578, 2016.

[39] L. Bao and S. S. Intille, “Activity Recognition from User-Annotated Acceleration Data,” in Pervasive Computing, A. Ferscha and F. Mattern, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, pp. 1–17.

[40] W. Xiao and Y. Lu, “Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine,” Mathematical Problems in Engineering, vol. 2015, p. 790412, 2015. [Online]. Available: https://doi.org/10.1155/2015/790412

[41] R. Igual, C. Medrano, and I. Plaza, “A comparison of public datasets for acceleration-based fall detection,” Medical Engineering and Physics, vol. 37, no. 9, pp. 870–878, 2015. [Online]. Available: http://dx.doi.org/10.1016/j.medengphy.2015.06.009

[42] S. Abbate, M. Avvenuti, P. Corsini, J. Light, and

[43] A. Vecchio, “Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care Using Wireless Sensor Network: a Survey,” Wireless Sensor Networks: Application-Centric Design, pp. 1–22, 2010.

[44] A. T. Özdemir, “An analysis on sensor locations of the human body for wearable fall detection devices: Principles and practice,” Sensors (Switzerland), vol. 16, no. 8, 2016.

[45] X. Sun, L. Qiu, Y. Wu, Y. Tang, and G. Cao, “Sleepmonitor: monitoring respiratory rate and body position during sleep using smartwatch,” in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 1, no. 3, 2017, pp. 1–22.

[46] B. Fida, I. Bernabucci, D. Bibbo, S. Conforto, and M. Schmid, “Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer,” Medical Engineering and Physics, vol. 37, no. 7, pp. 705–711, 2015. [Online]. Available: http://dx.doi.org/10.1016/j.medengphy.2015.04.005

[47] K. Maswadi, N. A. Ghani, S. Hamid, and M. B. Rasheed, “Human activity classification using Decision Tree and Naïve Bayes classifiers,” Multimedia Tools and Applications, vol. 80, no. 14, pp. 21 709–21 726, 2021.

[48] T. H. Lee, A. Ullah, and R. Wang, “Bootstrap Aggregating and Random Forest,” Advanced Studies in Theoretical and Applied Econometrics, vol. 52, pp. 389–429, 2020.

[49] D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “A Public Domain Dataset for Human Activity Recognition Using Smartphones,” in Proceedings of the 21th international European symposium on artificial neural networks, computational intelligence and machine learning, 2013, pp. 437–442.