Intelligent System for Fall Prediction Based on Accelerometer and Gyroscope of Fatal Injury in Geriatric
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Keywords
Abstract
Methods of prevention and equipment to reduce the risk of falls based on accelerometer and gyroscope sensor have developed rapidly because its operations are cheaper than video cameras. Improved accuracy of detection and fall prediction based on accelerometer and gyroscope sensor is carried out by utilizing Artificial Intelligence (AI) to predict falling patterns. However, the existing fall prediction system is less responsive and also has a low level of accuracy, sensitivity and specificity. The current system does not have a notification system to care givers or doctors in the hospital. To overcome the above problems, this study proposes the development of smart fall prediction system based on accelerometer and gyroscope for the prevention of fractures in geriatric populations (JaPiGi) which are accurate and have high sensitivity and specificity. This study uses Fuzzy Mamdani to recognize movements falling forward, falling sideways, sitting, sleeping, squatting and praying. The total data tested was 100 data from 10 participants. The introduction of this movement is based on 6 input variables from data of accelerometer and gyroscope sensor. To calculate the accuracy, precision, sensitivity and specificity in this study using the equation Receiver Operating Characteristic (ROC). Motion recognition is carried out 3 times with an average accuracy of 90%.
References
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Vilas-Boas, M., Silva, P., Cunha, S. R., & Correia, M. V. (2013, 3-7 July 2013). Monitoring of bedridden patients: Development of a fall detection tool. Paper presented at the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
Wang, R. d., Zhang, Y. l., Dong, L. p., Lu, J. w., Zhang, Z. q., & He, X. (2015, 13-16 Oct. 2015). Fall detection algorithm for the elderly based on human characteristic matrix and SVM. Paper presented at the Control, Automation and Systems (ICCAS), 2015 15th International Conference on.
Zheng, J., Zhang, G., & Wu, T. (2009, 11-13 June 2009). Design of Automatic Fall Detector for Elderly Based on Triaxial Accelerometer. Paper presented at the 2009 3rd36 International Conference on Bioinformatics and Biomedical Engineering. Marschollek, M. (2012). Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups. BMC Medical Informatics and Decision Making.
Tong, L. (2013). HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer. IEEE SENSORS JOURNAL.
Yang, S.-H. (2013). Fall-Prediction Algorithm Using a Neural Network for Safety Enhancement of Elderly. CACS International Automatic Control Conference (CACS)
Shi, G. (2009). Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier. IEEE Sensors J., vol. 9, no. 5
Simone Bova, P.C. (2010). A logical Analysis of Mamdani-type Fuzzy Inference I Theoritical Bases
Alwan, M., Rajendran, P. J., Kell, S., Mack, D., Dalal, S., Wolfe, M., & Felder, R. (2006, 0-00). A Smart and Passive Floor-Vibration Based Fall Detector for Elderly. Paper presented at the 2006 2nd International Conference on Information & Communication Technologies.
Amin, M. G., Zhang, Y. D., Ahmad, F., & Ho, K. C. D. (2016). Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring. IEEE Signal Processing Magazine, 33(2), 71-80. doi:10.1109/MSP.2015.2502784
Aziz, O., Musngi, M., Park, E. J., Mori, G., & Robinovitch, S. N. (2017). A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Medical & Biological Engineering & Computing, 55(1), 45-55. doi:10.1007/s11517-016-1504-y
Bourke, A. K., O’Brien, J. V., & Lyons, G. M. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & Posture, 26(2), 194-199. doi:10.1016/j.gaitpost.2006.09.012
Bouten, C. V. C., Koekkoek, K. T. M., Verduin, M., Kodde, R., & Janssen, J. D. (1997). A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Transactions on Biomedical Engineering, 44(3), 136-147. doi:10.1109/10.554760
Cheng, J., Chen, X., & Shen, M. (2013). A Framework for Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Accelerometer Signals. IEEE Journal of Biomedical and Health Informatics, 17(1), 38-45. doi:10.1109/TITB.2012.2226905.
Fuming, L., & Guo, J. (2011, 27-29 June 2011). Improving support vector machine by preprocessing data with decision tree. Paper presented at the Computer Science and Service System (CSSS), 2011 International Conference on.
Ghazal, M., Khalil, Y. A., Dehbozorgi, F. J., & Alhalabi, M. T. (2015, 19-21 Oct. 2015). An integrated caregiver-focused mHealth framework for elderly care. Paper presented at the Wireless and Mobile Computing, Networking and Communications (WiMob), 2015 IEEE 11th International Conference on.
He, J., Hu, C., & Wang, X. (2016). A Smart Device Enabled System for Autonomous Fall Detection and Alert. International Journal of Distributed Sensor Networks, 12(2), 2308183. doi:10.1155/2016/2308183
Hemalatha, C. S., & Vaidehi, V. (2013). Frequent Bit Pattern Mining Over Tri-axial Accelerometer Data Streams for Recognizing Human Activities and Detecting Fall. Procedia Computer Science, 19, 56-63. doi:http://dx.doi.org/10.1016/j.procs.2013.06.013
Hijaz, F., Afzal, N., Ahmad, T., & Hasan, O. (2010, 14-16 June 2010). Survey of fall detection and daily activity monitoring techniques. Paper presented at the Information and Emerging Technologies (ICIET), 2010 International Conference on.
Kavanagh, J. J., Barrett, R. S., & Morrison, S. (2004). Upper body accelerations during walking in healthy young and elderly men. Gait & Posture, 20(3), 291-298. doi:10.1016/j.gaitpost.2003.10.004
Khawandi, S., Ballit, A., & Daya, B. (2013, 27-29 Sept. 2013). Applying Machine Learning Algorithm in Fall Detection Monitoring System. Paper presented at the Computational35 Intelligence and Communication Networks (CICN), 2013 5th International Conference on.
Komariah, S. (2012). Peran Keperawatan Dalam Menurunkan Insiden Keselamatan Pasien. Retrieved from https://www.yumpu.com/id/document/view/40907908/perankeperawatan-dalam-menurunkan-insiden-keselamatan-pasien, accessed 15 Juni 2020
Maryam, S. R., Ekasari, M. F., Rosidawati., Jubaedi, A., & Batubara, I. (2008). Mengenal Usia Lanjut dan Perawatannya: Salemba Medika.
Miaou, S. G., Pei-Hsu, S., & Chia-Yuan, H. (2006, 2-4 April 2006). A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information. Paper presented at the 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2.
Nasution, A. H., & Emmanuel, S. (2007, 1-3 Oct. 2007). Intelligent Video Surveillance for Monitoring Elderly in Home Environments. Paper presented at the Multimedia Signal Processing, 2007. MMSP 2007. IEEE 9th Workshop on.
Noury, N., Rumeau, P., Bourke, A. K., ÓLaighin, G., & Lundy, J. E. (2008). A proposal for the classification and evaluation of fall detectors. IRBM, 29(6), 340-349. doi:http://dx.doi.org/10.1016/j.irbm.2008.08.002
Nugroho, W. (2008). Keperawatan Gerontologi. Jakarta: EGC.
Nukala, B. T., Shibuya, N., Rodriguez, A. I., Tsay, J., Nguyen, T. Q., Zupancic, S., & Lie, D. Y. C. (2014, 8-10 Oct. 2014). A real-time robust fall detection system using a wireless gait analysis sensor and an Artificial Neural Network. Paper presented at the Healthcare Innovation Conference (HIC), 2014 IEEE
Pierleoni, P., Pernini, L., Belli, A., Palma, L., Valenti, S., & Paniccia, M. (2015, 13-15 April 2015). SVM-based fall detection method for elderly people using Android low-cost smartphones. Paper presented at the Sensors Applications Symposium (SAS), 2015 IEEE.
Popescu, M., Li, Y., Skubic, M., & Rantz, M. (2008, 20-25 Aug. 2008). An acoustic fall detector system that uses sound height information to reduce the false alarm rate. Paper presented at the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
Schwarzmeier, A., Weigel, R., Fischer, G., & Kissinger, D. (2014, 19-23 Jan. 2014). A low power fall detection and activity monitoring system for nursing facilities and hospitals. Paper presented at the Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS), 2014 IEEE Topical Conference on.
Shin, J. H., Lee, B., & Park, K. S. (2011). Detection of Abnormal Living Patterns for Elderly Living Alone Using Support Vector Data Description. IEEE Transactions on Information Technology in Biomedicine, 15(3), 438-448. doi:10.1109/TITB.2011.2113352
Surana, J., Hemalatha, C. S., Vaidehi, V., Palavesam, S. A., & Khan, M. J. A. (2013, 25-27 July 2013). Adaptive learning based human activity and fall detection using fuzzy frequent pattern mining. Paper presented at the Recent Trends in Information Technology (ICRTIT), 2013 International Conference on.
Vilas-Boas, M., Silva, P., Cunha, S. R., & Correia, M. V. (2013, 3-7 July 2013). Monitoring of bedridden patients: Development of a fall detection tool. Paper presented at the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
Wang, R. d., Zhang, Y. l., Dong, L. p., Lu, J. w., Zhang, Z. q., & He, X. (2015, 13-16 Oct. 2015). Fall detection algorithm for the elderly based on human characteristic matrix and SVM. Paper presented at the Control, Automation and Systems (ICCAS), 2015 15th International Conference on.
Zheng, J., Zhang, G., & Wu, T. (2009, 11-13 June 2009). Design of Automatic Fall Detector for Elderly Based on Triaxial Accelerometer. Paper presented at the 2009 3rd36 International Conference on Bioinformatics and Biomedical Engineering. Marschollek, M. (2012). Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups. BMC Medical Informatics and Decision Making.
Tong, L. (2013). HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer. IEEE SENSORS JOURNAL.
Yang, S.-H. (2013). Fall-Prediction Algorithm Using a Neural Network for Safety Enhancement of Elderly. CACS International Automatic Control Conference (CACS)
Shi, G. (2009). Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier. IEEE Sensors J., vol. 9, no. 5
Simone Bova, P.C. (2010). A logical Analysis of Mamdani-type Fuzzy Inference I Theoritical Bases
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Published
Nov 30, 2021
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Amiroh, K., Rahmawati, D., & Wicaksono, A. Y. (2021). Intelligent System for Fall Prediction Based on Accelerometer and Gyroscope of Fatal Injury in Geriatric. Jurnal Nasional Teknik Elektro, 10(3). https://doi.org/10.25077/jnte.v10n3.936.2021
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