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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/ijerph110100643Search in Google Scholar
M.-C. Tsai, E. Chu, and C. R, Lee, “An automated sitting posture recognition system utilizing pressure sensors,” Sensors, vol. 23, no. 13, pp. 5894, 2023, doi:10.3390/s23135894Search in Google Scholar
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.2022.3216761Search in Google Scholar
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.442Search in Google Scholar
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.2011.05.015Search in Google Scholar
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), 2023Search in Google Scholar
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.2023.106374Search in Google Scholar
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.2016.7758131Search in Google Scholar
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/s18061745Search in Google Scholar
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/PACRIM47961.2019.8985070Search in Google Scholar
Q. Hu, X. Tang, and W. Tang, “A smart chair sitting posture recognition system using flex sensors and FPGA implemented artificial neural network,” IEEE Sensors Journal, vol. 20, no. 14, pp. 8007-8016, 2020, doi:10.1109/JSEN.2020.2980207Search in Google Scholar
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.2020.112451Search in Google Scholar
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/s22145337Search in Google Scholar
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/s18010208Search in Google Scholar
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.2020.3030096Search in Google Scholar
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:10.1177/15501329221102371Search in Google Scholar
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.2020.3016611Search in Google Scholar
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/electronics10151825Search in Google Scholar
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.2020.3023860Search in Google Scholar
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.2009.191Search in Google Scholar