Pre-Impact Fall Detection System Using Logistic Regression Model Pre-Impact Fall Detection System Using Logistic Regression Model

Main Article Content

Nuth Otanasap
Pornpimol Bangkomkun
Tanainan Tanantpapat

Abstract

The problem of falling among the elderly has significantly increased continuously. Researchers are trying to find a suitable way to monitor and alert before a fall to protect the body using an airbag, for example, to prevent the consequences of falls. In this paper, pre-fall surveillance and detection using three Kinnects installed at different angles enable detection without body tracking devices, which causes trouble.  In addition, Kinnect detection uses only the head position, the center of gravity, and the position of the feet to calculate the base support area, so there are no privacy violations. The model used to predict the fall event was a logistic regression analysis that used predictive variables of head displacement versus dynamic threshold and body center of gravity versus base support area. From the pre-impact fall predictor experimental results, the accuracy was 98.17%, the sensitivity was 87.97%, and the specificity was 98.98%. Therefore, it can be concluded that the developed system can detect pre-impact fall events using the logistic regression model and can function at the specified time according to the objective.

Article Details

How to Cite
Otanasap, N., Bangkomkun, P., & Tanantpapat, T. (2023). Pre-Impact Fall Detection System Using Logistic Regression Model: Pre-Impact Fall Detection System Using Logistic Regression Model. SAU JOURNAL OF SCIENCE & TECHNOLOGY, 9(1), 30–43. Retrieved from https://ph01.tci-thaijo.org/index.php/saujournalst/article/view/251805
Section
Research Article

References

Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., & Vecchio, A. (2012). A smartphone-based fall detection system. Pervasive and Mobile Computing, 8(6), 883-899.

Aggarwal, J. K., & Ryoo, M. S. (2011). Human activity analysis: A review. ACM Computing Surveys (CSUR), 43(3), 16.

Bourke, A. K., O'Donovan, K. J., Nelson, J. and OLaighin, G. M. (2008, August). Fall-detection Through Vertical Velocity Thresholding Using a Tri-axial Accelerometer Characterized Using an Optical Motion-capture System. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE (pp. 2832-2835). IEEE.

Bourke, A. K., O’brien, J. V., & Lyons, G. M. (2007). Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & posture, 26(2), 194-199.

Caon, M., Yue, Y., Tscherrig, J., Mugellini, E., & Abou Khaled, O. (2011, October). Context-aware 3d gesture interaction based on multiple kinects. In AMBIENT 2011, The First International Conference on Ambient Computing, Applications, Services and Technologies (pp. 7-12).

Dimou, A., Nemethova, O., & Rupp, M. (2005). Scene change detection for H. 264 using dynamic threshold techniques. na.

Huang, J., Di, P., Wakita, K., Fukuda, T., & Sekiyama, K. (2008, November). Study of fall detection using intelligent cane based on sensor fusion. In Micro-NanoMechatronics and Human Science, 2008. MHS 2008. International Symposium on (pp. 495-500). IEEE.

Kim, E., Helal, S. and Cook, D. (2010). Human Activity Recognition and Pattern Discovery. IEEE Pervasive Computing, 9(1).

Kwolek, B. and Kepski, M. (2016). Fuzzy inference-based fall detection using kinect and body-worn accelerometer. Applied Soft Computing, 40, 305-318.

Le, T. M., Van Tran, L., & Dao, S. V. T. (2021). A feature selection approach for fall detection using various machine learning classifiers. IEEE Access, 9, 115895-115908.

Li, S., Pathirana, P. N., & Caelli, T. (2014, August). Multi-kinect skeleton fusion for physical rehabilitation monitoring. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE (pp. 5060-5063). IEEE.

Luinge, H. J. and Veltink, P. H. (2005). Measuring Orientation of Human Body Segments Using Miniature Gyroscopes and Accelerometers. Medical and Biological Engineering and computing, 43(2), 273-282.

Madhubala, J. S., & Umamakeswari, A. (2015). A Vision based Fall Detection System for Elderly People. Indian Journal of Science and Technology, 8(S9), 172-180.

Maki, B. E. and McIlroy, W. E. (2003). Effects of Aging on Control of Stability. A textbook of audiological medicine: clinical aspects of hearing and balance, 671-690.

Mastorakis, G., & Makris, D. (2014). Fall detection system using Kinect’s infrared sensor. Journal of Real-Time Image Processing, 9(4), 635-646.

Noury, N., Rumeau, P., Bourke, A. K., ÓLaighin, G. and Lundy, J. E. (2008). A Proposal for The Classification and Evaluation of Fall Detectors. Irbm, 29(6), 340-349.

Otanasap, N. (2019). Pre-impact fall detection system using real time dynamic threshold and human body bounding box by multiple kinects. SAU JOURNAL OF SCIENCE & TECHNOLOGY, 5(1), 49-61.

Otanasap, N. and Boonbrahm, P. (2015, July). Real-time Action Detection from 3D Skeleton Data Using Multiple Kinects, In Proceeding of The 7th Walailak Research National Conference. (p. 188).

Otanasap, N. and Boonbrahm, P. (2015, Sep). Multiple Kinect for 3D Human Skeleton Posture Using Axis Replacement Method. the 14th IEEE International Symposium on Mixed and Augmented Reality.

Otanasap, N., & Boonbrahm, P. (2013, October). Survey of fall detection techniques based on computer vision. In Proceeding of The Second Asian Conference On Information Systems (pp. 351-355) THA

Otanasap, N., & Boonbrahm, P. (2017, February). Pre-impact fall detection system using dynamic threshold and 3D bounding box. In Eighth International Conference on Graphic and Image Processing (pp. 102250D-102250D). International Society for Optics and Photonics.

Otanasap, N., & Boonbrahm, P. (2019). Fuzzy Inference Based Pre-impact Fall Detection System Using Dynamic Threshold. ASEAN Journal of Scientific and Technological Reports, 22(2), 44-52.

Rougier, C., Meunier, J., St-Arnaud, A. and Rousseau, J. (2006, August). Monocular 3D Head Tracking to Detect Falls of Elderly People. In Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE (pp. 6384-6387). IEEE.

Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2006, August). Monocular 3D head tracking to detect falls of elderly people. In Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE (pp. 6384-6387). IEEE.

Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., ... and Blake, A. (2011, June). Real-time Human Pose Recognition in Parts from Single Depth Images. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 1297-1304). Ieee.

Stone, E. E., & Skubic, M. (2015). Fall detection in homes of older adults using the microsoft kinect. Biomedical and Health Informatics, IEEE Journal of, 19(1), 290-301.

Tolkiehn, M., Atallah, L., Lo, B., & Yang, G. Z. (2011, August). Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE (pp. 369-372). IEEE.

World Health Organization. (2012). Good health adds life to years. Global brief for World Health Day 2012. Geneva, Switzerland: World Health Organization. Accessed July, 1, 2012.

Wu, G. (2000). Distinguishing fall activities from normal activities by velocity characteristics. Journal of biomechanics, 33(11), 1497-1500.

Yang, M. T., & Chuang, M. W. (2013). Fall risk assessment and early-warning for toddler behaviors at home. Sensors, 13(12), 16985-17005.

Youm, S., & Kim, W. (2003, July). Dynamic threshold method for scene change detection. In Multimedia and Expo, 2003. ICME'03. Proceedings. 2003 International Conference on (Vol. 2, pp. II-337). IEEE.

Zadeh, L. A. (1976). A fuzzy-algorithmic approach to the definition of complex or imprecise concepts. In Systems Theory in the Social Sciences (pp. 202-282). Birkhäuser, Basel.