A comparative study on real-time sitting posture monitoring systems using pressure sensors

[1] 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 Search in Google Scholar

[2] 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/s23135894 Search in Google Scholar

[3] 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 Search in Google Scholar

[4] 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 Search in Google Scholar

[5] 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 Search in Google Scholar

[6] 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 Search in Google Scholar

[7] 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 Search in Google Scholar

[8] 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 Search in Google Scholar

[9] 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 Search in Google Scholar

[10] 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 Search in Google Scholar

[11] 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.2020.2980207 Search in Google Scholar

[12] 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 Search in Google Scholar

[13] 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 Search in Google Scholar

[14] 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 Search in Google Scholar

[15] 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 Search in Google Scholar

[16] 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 Search in Google Scholar

[17] 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 Search in Google Scholar

[18] 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 Search in Google Scholar

[19] 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 Search in Google Scholar

[20] 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 Search in Google Scholar