Smartphone Based Data Mining for Fall Detection: Analysis and Design
Falls can be devastating to the affected individual, yet a common event and hence one of the major causes of injury or disability within the aged population in Malaysia and worldwide. This paper aims to detect human fall utilizing the built inertial measurement unit (IMU) sensors of a smartphone att...
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| Published in | Procedia computer science Vol. 105; pp. 46 - 51 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.01.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1877-0509 1877-0509 |
| DOI | 10.1016/j.procs.2017.01.188 |
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| Abstract | Falls can be devastating to the affected individual, yet a common event and hence one of the major causes of injury or disability within the aged population in Malaysia and worldwide. This paper aims to detect human fall utilizing the built inertial measurement unit (IMU) sensors of a smartphone attached to the subject's body with the signals wirelessly transmitted to remote PC for processing. Matlab's mobile and the Smartphone Sensor Support is used to acquire the data from the smartphone which is then analysed to design an algorithm for the detection of fall. Falls in human are usually characterized by large acceleration. However, focusing only on a large value of the acceleration can result in many false positives from fall-like activities such as sitting down quickly and jumping. Thus, in this work, a threshold based fall detection algorithm is implemented while a supervised machine learning algorithm is used to classify activity daily living (ADL). This combination has been found effective in increasing the accuracy of the fall detection. The aim is to develop and verify the high precision detection algorithm using Matlab Simulink, followed by a few real time testing. |
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| AbstractList | Falls can be devastating to the affected individual, yet a common event and hence one of the major causes of injury or disability within the aged population in Malaysia and worldwide. This paper aims to detect human fall utilizing the built inertial measurement unit (IMU) sensors of a smartphone attached to the subject's body with the signals wirelessly transmitted to remote PC for processing. Matlab's mobile and the Smartphone Sensor Support is used to acquire the data from the smartphone which is then analysed to design an algorithm for the detection of fall. Falls in human are usually characterized by large acceleration. However, focusing only on a large value of the acceleration can result in many false positives from fall-like activities such as sitting down quickly and jumping. Thus, in this work, a threshold based fall detection algorithm is implemented while a supervised machine learning algorithm is used to classify activity daily living (ADL). This combination has been found effective in increasing the accuracy of the fall detection. The aim is to develop and verify the high precision detection algorithm using Matlab Simulink, followed by a few real time testing. |
| Author | Hakim, Abdul Ibrahim, B.S.K.K. Shanta, Shahnoor Huq, M. Saiful |
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| Cites_doi | 10.1109/IEMBS.2003.1280846 10.1109/ICTTA.2006.1684511 10.1258/1357633054068946 10.1109/BSN.2009.46 10.1007/978-3-642-02481-8_120 10.1109/ICSMC.2012.6378165 10.1258/1357633001934483 10.1007/978-3-642-02710-9_32 10.1371/journal.pone.0036556 10.1016/j.neucom.2011.09.037 10.1016/j.gaitpost.2006.09.012 10.1145/1555816.1555835 10.1109/IEMBS.2009.5334912 10.1145/1964897.1964918 10.1186/1475-925X-12-66 10.1007/s11036-008-0112-y 10.1016/j.medengphy.2006.12.001 |
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| References_xml | – reference: Dai, J., Bai, X., Yang, Z., Shen, Z. & Xuan, D. (2010). PerFallD: A pervasive fall detection system using mobile phones. Paper presented at the Pervasive Computing and Communications Workshops (PERCOM Workshops), 2010 8th IEEE International Conference on. – reference: pp. 796-799, Springer. – volume: 26 start-page: 194 year: 2007 end-page: 199 ident: bib0085 article-title: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm publication-title: Gait & posture. – reference: Sposaro, F. & Tyson, G. (2009). iFall: an Android application for fall monitoring and response. 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Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. Paper presented at the Wearable and Implantable Body Sensor Networks, 2009 BSN 2009 Sixth International Workshop on. – reference: Li, Q., Stankovic, J.A., Hanson, M.A., Barth, A.T., Lach, J. & Zhou, G. (2009). Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. Paper presented at the Wearable and Implantable Body Sensor Networks, 2009 BSN 2009 Sixth International Workshop on. – reference: Kamarulzaman, K. (2007) Adult learning for people with disabilities in Malaysia: Provisions and services, – reference: Kim, J., Kwak, H., Lee, H., Seo, K., Lim, B., Lee, M., Lee, J. & Roh, K. (2012). Balancing control of a biped robot. Paper presented at the Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on. – reference: Maenaka, K. (2008). MEMS inertial sensors and their applications. 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| Title | Smartphone Based Data Mining for Fall Detection: Analysis and Design |
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