A Real-Time Mobility-Related Activity Tracking System for Mobility and Fall Risk Assessment in Elderly People

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กิตติศักดิ์ บัวบก
สุภัสสรา จีนบุญมี
วัศวี แสนศรีมหาชัย
มานะชัย โต๊ะชูดี


- Over the last decade, population structure of Thailand has changed dramatically where the proportion of elderly people (persons aged 60 or over) in the population increases rapidly and continuously. It is expected that Thailand will become a super aged society by 2040 or about 20 years from now. The elderly are usually faced with many problems resulting from the deterioration of health with increasing age. One of the major problems in the elderly is falls – balance and gait disorders in the elderly. Falls have significant effects on both physiological and psychological condition of elderly people. They consequently lead to fracture, serious injuries, disability or eventually death. To support medical staffs and caregivers who provide care to elderly people, we propose an innovative software system that can analyze physical activities and assess the risk of falls in elderly remotely. Our system utilizes acceleration force data and gyroscope data derived from a mobile phone attached to the body of the elderly in order to classify mobility-related activities of the elderly in real-time. It can store a significant amount of mobility-related activity information and predict the risk of falls in elderly people. The experimental results demonstrate that our proposed system can work efficiently in real environment with a high activity recognition rate - about 93.90% for overall accuracy.


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บัวบกก., จีนบุญมีส., แสนศรีมหาชัยว., and โต๊ะชูดีม., “A Real-Time Mobility-Related Activity Tracking System for Mobility and Fall Risk Assessment in Elderly People”, JIST, vol. 6, no. 1, pp. 16-24, Jun. 2016.
Research Article: Soft Computing (Detail in Scope of Journal)


1. สำนักงานสถิติแห่งชาติ, ผลการสำรวจประชากรสูงอายุ พ.ศ.2557, http://service.nso.go.th/nso/nsopublish/themes/files/elderlyworkFullReport57-1.pdf (เข้าถึงครั้งล่าสุดเมื่อวันที่ 8 กันยายน 2559).

2. สถาบันวิจัยประชากรและสังคม มหาวิทยาลัยมหิดล, การสูงวัยของประชากรไทย พ.ศ. 2557, http://www.ipsr.mahidol.ac.th/ipsrbeta/th/BookReport.aspx, (เข้าถึงครั้งล่าสุดเมื่อวันที่ 8 กันยายน 2559).

3. Y.-W. Bai, S.-C. Wu and C.-L. Tsai, “Design and implementation of a fall monitor system by using a 3-axis accelerometer in a smart phone”, IEEE Transactions on Consumer Electronics, Vol. 58, No. 4, pp. 1269-1275, Nov. 2012.

4. S. Dernbach, B. Das, C. N. Krishnan, B. L. Thomas and D. J. Cook, “Simple and Complex Activity Recognition through Smart Phones”, in Proceedings of IE 2012, 2012, pp. 214-221.

5. C. Jalayondeja, “Falls screening by Timed Up and Go (TUG)”, Journal of Medical Technology and Physical Therapy, Vol 26, No. 1, pp. 5 -16.

6. P. Jantaraprim, P. Phukpattaranont, C. Limsakul and B. Wongkittisuksa, “Fall detection for the elderly using a support vector machine”, International Journal of Soft Computing and Engineering, Vol. 2, No. 1, Mar. 2012, pp. 484-490.

7. J. R. Kwapisz, G. M. Weiss and S. A. Moore, “Activity Recognition Using Cell Phone Accelerometers”, ACM SIGKDD Explorations Newsletter, Vol. 12, No. 2, pp. 74-82.

8. O. D. Lara and M. A. Labrador, “A survey on human activity recognition using wearable sensors”, IEEE Communications Surveys Tutorials, Vol. 15, No. 3, Jan. 2013, pp. 1192-1209.

9. J. W. Lockhart, G. M. Weiss, J. C. Xue, S. T. Gallagher, A. B. Grosner, and T. T. Pulickal, “Design considerations for the wisdm smart phonebased sensor mining architecture”, in Proceedings of SensorKDD’11, 2011, pp. 25-33.

10. S. Mathias, U. S. Nayak and B. Lsaacs, “Balance in elderly patients: the get-up and go test”, Arch Phys Med Rehabil, Vol. 67, No. 6, pp. 387-389.

11. D. Podsiadlo and S. Richardson, The time “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc, Vol. 39, No.2, Feb. 1991, pp. 142-148.

12. N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman, “Activity recognition from accelerometer data”, in Proceedings of IAAI 2005 - Volume 3, 2005, pp. 1541–1546.

13. F. Sposaro and G. Tyson, “iFall: An android application for fall monitoring and response”, Proceedings of EMBC’09, 2009, pp. 6119-6122.