J. Withall, A. Stathi, M. Davis, J. Coulson, J. L. Thompson, and K. R. Fox, “Objective indicators of physical activity and sedentary time and associations with subjective well-being in adults aged 70 and over,” International Journal of Environmental Research and Public Health, vol. 11, no. 1, pp. 643-656, 2014, doi:10.3390/ijerph110100643
I. Wijegunawardana, R. Ranaweera, and R. Gopura, “Lower extremity posture assistive wearable devices: A review,” IEEE Transactions on Human-Machine Systems, vol. 53, no.1, pp. 98-112, 2023, doi:10.1109/THMS.2022.3216761
A. Kulikajevas, R. Maskeliunas, and R. Damaševičius, “Detection of sitting posture using hierarchical image composition and deep learning,” PeerJ Computer Science, vol. 7, pp. e442, 2021, doi:10.7717/peerj-cs.442
M. Taieb-Maimon, J. Cwikel, B. Shapira, and I. Orenstein, “The effectiveness of a training method using self-modeling webcam photos for reducing musculoskeletal risk among office workers using computers,” Applied Ergon, vol. 43, no. 2, pp. 376-385, 2021, doi:10.1016/j.apergo.2011.05.015
J. Yan, and A. Wang, “iGuard: An intelligent sitting posture monitoring system with pressure sensors,” 2023 Third International Conference on Computer Vision and Pattern Analysis (ICCPA), 2023
L. Li, G. Yang, Y. Li, D. Zhu, and L. He, “Abnormal sitting posture recognition based on multi-scale spatiotemporal features of skeleton graph,” Engineering Applications of Artificial Intelligence, vol. 123, pp. 106374, 2023, doi:10.1016/j.engappai.2023.106374
S. Ma, W. H. Cho, C. H. Quan, and S. Lee, “A sitting posture recognition system based on 3-axis accelerometer,” 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2016, doi:10.1109/CIBCB.2016.7758131
Z. Qian, A. Bowden, D. Zhang, J. Wan, W. Liu, X. Li, D. Baradoy, and D. T. Fullwood, “Inverse piezoresistive nanocomposite sensors for identifying human sitting posture,” Sensors, vol. 18, no. 6, pp. 1745, 2018, doi: 10.3390/s18061745
L. Feng, Z. Li, and C. Liu, “Are you sitting right? Sitting posture recognition using RF signals”, 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), 2019, doi:10.1109/PACRIM47961.2019.8985070
A. Anwarya, D. Cetinkaya, M. Vassalloc, and H. Bouchachia, “Smart-cover: A real-time sitting pos-ture monitoring system,” Sensors and Actuators A: Physical, vol. 317, pp. 112451, 2021, doi:10.1016/j.sna.2020.112451
K. Bourahmoune, K. Ishac, and T. Amagasa, “Intelligent posture training: machine-learning-powered human sitting posture recognition based on a pressure-sensing IoT cushion,” Sensors, vol. 22, no. 14, pp. 5337, 2022, doi:10.3390/s22145337
J. Roh, H. Park, K. J. Lee, J. Hyeong, S. Kim and B. Lee, “Sitting posture monitoring system based on a low-cost load cell using machine learning,” Sensors, vol. 18, no. 1, pp. 208, 2018, doi:10.3390/s18010208
H. Jeong and W. Park, “Developing and evaluating a mixed sensor smart chair system for real-time posture classification: Combining pressure and distance sensors,” IEEE Journal of Biomedical and Health Informatics, vol. 25, pp. 1805-1813, 2020, doi:10.1109/JBHI.2020.3030096
L. M. Ang, K. P. Seng, and M. Wachowicz, “Embedded intelligence and the data-driven future of application-specific internet of things for smart environments,” International Journal of Distributed Sensor Networks, vol. 18, no. 6, pp. 15501329221102371, 2022, doi:10.1177/15501329221102371
J. Wang, B. Hafidh, H. Dong, and A. El Saddik, “Sitting posture recognition using a spiking neural network,” IEEE Sensors Journal, vol. 21, no. 2, pp. 1779-1786, 2020, doi:10.1109/JSEN.2020.3016611
F. Luna-Perejón, J. M. Montes-Sánchez, L. Durán-López, A. Vazquez-Baeza, I. Beasley-Bohórquez, and J. L. Sevillano-Ramos, “IoT device for sitting posture classification using artificial neural networks,” Electronics, vol. 10, no. 15, pp. 1825, 2021, doi:10.3390/electronics10151825
A. Wang, S. Zhao, C. Zheng, H. Chen, L. Liu, and G. Chen, “HierHAR: Sensor-based data-driven hierarchical human activity recognition,” IEEE Sensors Journal, vol. 21, no. 3, pp. 3353-3365, 2021, doi:10.1109/JSEN.2020.3023860
S. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, 2010, doi:10.1109/TKDE.2009.191