Wireless Fall Warning System with Real-Time Motion Monitoring
Main Article Content
Abstract
Fall is one of the major causes of serious injury, which include fractures, traumatic brain injury, and death to the elders or people who live alone. A wearable fall detection system is becoming a potential solution thanks to its popular accelerometer sensor and easily implementation. In this study, we proposed a design and implementation of wireless fall warning system with real – time motion monitoring. The wearable motion sensor module received real-time human motion is used in combination with a smartphone through wireless Bluetooth connection. A Fall warning application on smartphone is responsible for wirelessly send fall warning message of subject through GSM service and Google map – based fall location. The implementation of our proposed system shows a great potential solution in supporting elders, decreasing deaths and improve their lives qualities.
Keywords
Fall people, Elders, Fall detection, Real-time motion monitoring
Article Details
References
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[2] Yavuz, G., Kocak, M., Ergun, G., Alemdar, H., Yalcin, H., Incel, O.O., Ersoy, C.; A smartphone based fall detector with online location support; In Proceedings of the International Workshop on Sensing for App Phones, Zurich, Switzerland, 2 November (2010) 31–35.
[3] Yoosuf Nizam, Mohd Norzali Haji Mohd, M. Mahadi Abdul Jamil; Human fall detection from Depth Images using Position and Velocity of Subject; Procedia Computer Science 105 (2017) 131–137.
[4] Neslihan Özge Çiftçi, Emre Çiftçi, şÜkrü Ökkesim; A new fall detection system design for elderly people; Medical Technologies National Conferences (TIPTEKNO) DOI: 10.1109/TIPTEKNO.2015.7374107.
[5] He, Y., Li, Y.; Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone; Int. J. Distrib. Sens. Netw. (2013). doi:10.1155/2013/48158013.
[6] Igual, R., Plaza, I., Martín, L., Corbalan, M., Medrano, C.; Guidelines to Design Smartphone Applications for People with Intellectual Disability: A Practical Experience; In Ambient Intelligence-Software and Applications; Springer: Berlin, Germany (2013) 65–69.
[7] De Urruti Breton, Z.S., Hernández, F.J., Zorrilla, A.M., Zapirain, B.G.; Mobile communication for intellectually challenged people: A proposed set of requirements for interface design on touch screen devices; Commun. Mobile Comput 1 (2012) 1–4.
[8] Abdul Hakim, M., Saiful Huq, Shahnor Shanta, B.S.K.K. Ibrahim; 2016 Smartphone Based Data Mining for Fall Detection: Analysis and Design; Procedia Computer Science 105 (2017) 46–51.
[9] Ngoc Phuc Pham, Hung Viet Dao, Ha Ngoc Phung, Huy Van Ta, Man Hoang Nguyen, Tram Thi Hoang; Classification Different Types of Fall For Reducing False Alarm Using Single Accelerometer; ICCE (2018) 316–321.