Suhwan Lee, Hanif Bhuiyan, Dongik Oh, Kyeong-Jin Oh, Yunyoung Nam, Myung-Duk Hong, Geun-Sik Jo, Journal of Internet Computing and Services, Vol. 18, No. 3, pp. 11-18, Jun. 2017
10.7472/jksii.2017.18.3.11, Full Text:
Keywords: Fall Detection, Acceleration measurement, Frequency spectrum, Pattern matching, Wikipedia, Semantic relation, Identification, Infobox Template, Information Extraction, Semi-structured data

Abstract

To the elderly, sudden falls are one of the most frightening accidents. If an accident occurs, a prompt action has to be taken to deal with the situation. Recently, there have been a number of attempts to detect sudden falls using acceleration sensors embedded in the mobile devices, such as smart phones and wrist-bands. However, using the sensor readings only, the detection rate of the falls is around 65%. Ordinary daily activities such as running or jumping could not be well distinguished from the falls. In this paper, we describe our attempts on improving the fall-detection rate. We implemented a wrist-band fall detection module, using a three-axis acceleration sensor. With the pattern matching on the fall signal-strength frequency spectrum, in addition to the conventional signal strength measurement, we could improve the detection rate by 9% point. Furthermore, by applying two wrist-bands in the experiment, we could further improve the detection rate to 82%.

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