Abstract
Falls are a key cause of significant health problems, especially for elderly people who live alone. Falls are a leading cause of accidental injury and death. To help assist the elderly, we propose a system to detect daily activities and in-house location of a user by means of a smartphone’s sensor and Wi-Fi access points. We applied data mining techniques to classify activity detection (e.g., sitting, standing, lying down, walking, running, walking up/downstairs, and falling) and in-house location detection. Health risk level configurations (threshold model) are applied for unhealthy activity detection with an alarm sounding and also short messages sent to those who have responsibility such as a caregiver or a doctor. Moreover, we provide various forms of easy to understand visualization for monitoring and include health risk level summary, daily activity summary, and in-house location summary.
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World Health Organization, WHO Global Report on Falls Prevention in Older Age, http://www.who.int/ageing/publications/Falls_prevention7March.pdf?ua=1
Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Web–based Injury Statistics Query and Reporting System (WISQARS) (2013), http://www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html
ASTV-Manager Online, Eight ways to prevent the elderly falls (May 26, 2014), http://www.manager.co.th/QOL/ViewNews.aspx?NewsID=9570000058682
Jian, H., Chen, H., Yang, L.: An autonomous fall detection and alerting system based on mobile and ubiquitous. In: IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC), pp. 539–543 (2013)
Daniel, R., Albert, S., Carlos, P., Andreu, C., Joan, C., Alejandro, R.: SVM-based posture identification with a single waist-located tri-axial accelerometer. Expert Systems with Applications 40(18), 7203–7211 (2013)
Jovanov, E., Milosevic, M., Milenkovi, A.: A mobile system for assessment of physiological response to posture transitions. In: Proceeding of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2013)
Zigel, Y.: A method for automatic fall detection of elderly people using floor vibration and sound. IEEE Transactions on Biomedical Engineering 56(12), 2858–2867 (2009)
Jonghun, B., Byoung-Ju, Y.: Posture monitoring system for context awareness in mobile computing. IEEE Transactions on Instrumentation and Measurement 59(6), 1589–1599 (2010)
Jennifer, R., Gary, M., Samuel, A.: Activity recognition using cell-phone accelerometers. ACM SIGKDD Explorations Newsletter 12, 74–82 (2010)
Yue, S., Yuanchun, S., Jie, L.: A Rotation based method for detecting on-body positions of mobile devices. In: Proceeding of the 13th International Conference on Ubiquitous Computing, pp. 559–560 (2011)
Petar, M., Roman, M., Marko, J., Hrvoje, H., Aimé, L., Patrizia, V.: System for monitoring and fall detection of patients using mobile 3-axis accelerometers sensors. In: IEEE International Workshop on Medical Measurements and Applications Proceedings (MeMeA), pp. 456–459 (2011)
Stefano, A., Marco, A., Francesco, B., Guglielmo, C., Paolo, C., Alessio, V.: A smartphone-based fall detection system. Pervasive and Mobile Computing 8, 883–899 (2012)
Ying-Wen, B., Siao-Cian, W., Cheng-Lung, T.: Design and implementation of a fall monitor system by using a 3-axis accelerometer in a smart phone. In: Proceeding of the IEEE 16th International Symposium on Consumer Electronics (ISCE) (2012)
Lee, H., Lee, S., Choi, Y., Youngwan, S.: A new posture monitoring system for preventing physical illness of smartphone users. In: Proceeding of the 2013 IEEE Consumer Communications and Networking Conference (CCNC), pp. 657–661 (2013)
Lina, T., Quanjun, S., Yunjian, G., Ming, L.: HMM-based human fall detection and prediction method using tri-axial accelerometer. Sensors Journal 3, 1849–1856 (2013)
Stephen, A., Mark, V., Konrad, P.: Hand, belt, pocket or bag: practical activity tracking with mobile phones. Journal of Neuroscience Methods 231, 22–30 (2014)
Guiry, J.J., van de Ven, P., Nelson, J., Warmerdam, L., Riper, H.: Activity recognition with smartphone support. Medical Engineering & Physics 36, 670–675 (2014)
Quoc, T.H., Uyen, D.N., Su, V.T., Afshin, N., Binh, Q.T.: Fall detection system using combination accelerometer and gyroscope. In: International Conference on Advances in Electronic Devices and Circuits – EDC, Kuala Lumpur, Malaysia (2013)
Paliyawan, P., Nukoolkit, C., Mongkolnam, P.: Prolonged sitting detection for office workers syndrome prevention using Kinect. In: Proceeding of the 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (2014)
Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews 37(6), 1067–1080 (2007)
Premchaisawatt, S.: Enhancing indoor positioning based on partitioning cascade machine learning models. In: Proceeding of the 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (2014)
Zhongtang, Z., Yiqiang, C., Shuangquan, W., Zhenyu, C.: FallAlarm Smart Phone Based Fall Detecting and Positioning System. Procedia Computer Science 01, 617–624 (2012)
The University of Waikato, Weka 3 - Data Mining with Open Source Machine Learning Software in Java, http://www.cs.waikato.ac.nz/ml/wek/
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Sukreep, S., Mongkolnam, P., Nukoolkit, C. (2015). Detect the Daily Activities and In-house Locations Using Smartphone. In: Unger, H., Meesad, P., Boonkrong, S. (eds) Recent Advances in Information and Communication Technology 2015. Advances in Intelligent Systems and Computing, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-319-19024-2_22
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DOI: https://doi.org/10.1007/978-3-319-19024-2_22
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19023-5
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