Sitting Posture Detection and Classification Using Machine Learning Algorithms on RapidMiner
Abstract
Integrating pressure sensors into cushion pads presents a viable posture monitoring and classification solution in innovative health care and ergonomic design. In this study, a cushion pad with a pressure sensor implanted that can recognize and classify different postures using machine learning techniques is developed and evaluated. The principal objective is to augment postural awareness and avoid disorders of the muscles. The cushion pad system was created and used by combining software algorithms with hardware sensors. Using a variety of machine learning approaches, RapidMiner, a data science platform, was used to analyze the pressure data to classify postures. The following algorithms are tested using cross-validation for a robust evaluation: Decision Tree, Naive Bayes, Neural Network, Random Forest, and K-Nearest Neighbors (K-NN). The outcomes showed that the various algorithms' levels of accuracy varied. The Naive Bayes algorithm demonstrated a lesser accuracy of 55.83% compared to the Decision Tree algorithm's 84.49% accuracy. The Random Forest algorithm surpassed the others with an accuracy of 85.98%, while the Neural Network approach produced an accuracy of 82.26%. The k-NN algorithm also yielded promising results, with an accuracy of 82.01%. According to these results, the Random Forest algorithm outperforms the Decision Tree algorithm for posture categorization in this specific example. A workable approach for enhancing ergonomic health and avoiding posture-related illnesses is to integrate such machine learning models into a cushion pad with pressure sensor integration that can significantly help proactive posture management.
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Lazko O, Byshevets, N., Plyеshakova, O., Lazakovych, Y., Kashuba, V., Grygus, I., and Yarmolinsky, L. Determinants of office syndrome among working age women. J Phys Educ Sport., Vol.21, pp. 2827-2834, 2021.
Biernat E, Tomaszewski P, Milde K. Physical activity of office workers. Biol Sport. Vol. 27, no. 4, 2010.
Feng B, Chen, K., Zhu, X., Ip, W. Y., Andersen, L. L., Page, P., and Wang, Y. Prevalence and risk factors of self-reported wrist and hand symptoms and clinically confirmed carpal tunnel syndrome among office workers in China: a cross-sectional study. BMC Public Health, Vol. 21, pp.1-10, 2021.
Demissie, B., Yenew, C., Alemu, A., Bantie, B., Sume, B. W., Deml, Y. A., and Tegegne, E. Carpal tunnel syndrome and its associated factors among computer user bankers in South Gondar Zone, Northwest Ethiopia, 2021: a cross sectional study. BMC Musculoskelet Disord. Vol. 24, no. 1, pp. 828, 2023.
Michalchuk, V. F., Lee, S. J., Waters, C. M., Hong, O. S., and Fukuoka, Y. Systematic Review of the Influence of Physical Work Environment on Office Workers’ Physical Activity Behavior. Work Health Saf. Vol. 70, no. 2, pp. 97-119, 2022.
Lindberg, C. M., Srinivasan, K., Gilligan, B., Razjouyan, J., Lee, H., Najafi, B., and Sternberg, E. M. Effects of office workstation type on physical activity and stress. Occup Environ Med. Vol. 75, no. 10, pp. 689-695, 2018.
Landais, L. L., Jelsma, J. G., Dotinga, I. R., Timmermans, D. R., Verhagen, E. A., and Damman, O. C. Office workers' perspectives on physical activity and sedentary behaviour: a qualitative study. BMC Public Health. Vol. 22, no.1, pp. 621, 2022.
Nguyen TM, Nguyen VH, Kim JH. Physical exercise and health-related quality of life in office workers: a systematic review and meta-analysis. Int J Environ Res Public Health. Vol. 18, no. 7, pp. 3791, 2021.
Tan HZ, Slivovsky LA, Pentland A. A sensing chair using pressure distribution sensors. IEEE/ASME Trans Mechatron. Vol. 6, no. 3, pp. 261-268, 2001.
Ma, C., Li, W., Gravina, R., Cao, J., Li, Q., and Fortino, G.. Activity level assessment using a smart cushion for people with a sedentary lifestyle. Sensors. Vol. 17, no. 10, pp. 2269, 2017.
Huang M, Gibson I, Yang R. Smart chair for monitoring of sitting behavior. KnE Eng. Pp. 274-80, 2017.
Ishac K, Suzuki K. Lifechair: A conductive fabric sensor-based smart cushion for actively shaping sitting posture. Sensors. Vol. 18, no. 7, pp. 2261, 2018.
Yuan L, Li J. Smart cushion based on pressure sensor array for human sitting posture recognition. IEEE Sensors, 2021.
He Y. Detecting passenger postures by pressure sensors under the cushion of an aircraft seat. 2020.
Ma, C., Li, W., Gravina, R., and Fortino, G. Posture detection based on smart cushion for wheelchair users. Sensors. Vol. 17, no. 4, pp.719, 2017.
Aminosharieh Najafi, T., Abramo, A., Kyamakya, K., and Affanni, A. Development of a smart chair sensors system and classification of sitting postures with deep learning algorithms. Sensors. Vol. 22, no. 15, pp. 5585, 2022.
Bibbo, D., Carli, M., Conforto, S., and Battisti, F. A sitting posture monitoring instrument to assess different levels of cognitive engagement. Sensors. Vol. 19, no. 3, pp. 455, 2019.
Xu, W., Huang, M. C., Amini, N., He, L., and Sarrafzadeh, M. ecushion: A textile pressure sensor array design and calibration for sitting posture analysis. IEEE Sens J. Vol. 13, no. 10, pp. 3926-3934, 2013.
Martínez-Estrada, M., Vuohijoki, T., Poberznik, A., Shaikh, A., Virkki, J., Gil, I., and Fernández-García, R. A Smart Chair to Monitor Sitting Posture by Capacitive Textile Sensors. Materials. Vol.16, no 13, pp. 4838, 2023.
Haller M, et al. Finding the right way for interrupting people improving their sitting posture. Human-Computer Interaction–INTERACT 2011: 13th IFIP TC 13 International Conference, Lisbon, Portugal, September 5-9, 2011, Proceedings, Part II 13. Springer Berlin Heidelberg, 2011.
Cirkit Designer [Software]. Version 2024. New York, NY: Cirkit Design LLC, 2024.
Forero JD, Valencia GE, Obregón LG. Methodology of calibration of FSR sensor for seat occupancy detection in vehicles. Indian J Sci Technol. 2018, pp.1-7, 2018.
Schofield, J. S., Evans, K. R., Hebert, J. S., Marasco, P. D., and Carey, J. P. The effect of biomechanical variables on force sensitive resistor error: Implications for calibration and improved accuracy. J Biomech. Vol.49, no. 5, pp.786-792, 2016.
McLean KM, Pasulka AL, Bockmon EE. A low-cost, accessible, and high-performing Arduino-based seawater pH control system for biological applications. HardwareX. 2021;10:e00247.
Trevathan J, Schmidtke S. Open-source Internet of Things remote aquatic environmental sensing. HardwareX. 2022;12:e00336.
RapidMiner Studio [Software]. Version 2024. Boston, MA: RapidMiner, Inc; 2024.
Zemp R, Tanadini M, Plüss S, Schnüriger K, Singh NB, Taylor WR, Lorenzetti S. Application of machine learning approaches for classifying sitting posture based on force and acceleration sensors. BioMed research international. 2016, no1, pp. 5978489, 2016.
Roh J, Park HJ, Lee KJ, Hyeong J, Kim S, Lee B. Sitting posture monitoring system based on a low-cost load cell using machine learning. Sensors, Jan 12, Vol.18, no. 1, pp.208, 2018.
Kim YM, Son Y, Kim W, Jin B, Yun MH. Classification of children’s sitting postures using machine learning algorithms. Applied Sciences. 2018 Aug 1;8(8):1280.
Ahmad J, Andersson H, Siden J. Screen-printed piezoresistive sensors for monitoring pressure distribution in wheelchair. IEEE Sensors Journal. Vol.19, no. 6, pp. 2055-2063, 2018.
Luna-Perejón F, Montes-Sánchez JM, Durán-López L, Vazquez-Baeza A, Beasley-Bohórquez I, Sevillano-Ramos JL. Iot device for sitting posture classification using artificial neural networks. Electronics. Vol.10, no. 15, pp.1825, 2021.
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