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http://dx.doi.org/10.30693/SMJ.2021.10.3.31

Fall detection based on acceleration sensor attached to wrist using feature data in frequency space  

Roh, Jeong Hyun (서경대학교 컴퓨터공학과)
Kim, Jin Heon (서경대학교 컴퓨터공학과)
Publication Information
Smart Media Journal / v.10, no.3, 2021 , pp. 31-38 More about this Journal
Abstract
It is hard to predict when and where a fall accident will happen. Also, if rapid follow-up measures on it are not performed, a fall accident leads to a threat of life, so studies that can automatically detect a fall accident have become necessary. Among automatic fall-accident detection techniques, a fall detection scheme using an IMU (inertial measurement unit) sensor attached to a wrist is difficult to detect a fall accident due to its movement, but it is recognized as a technique that is easy to wear and has excellent accessibility. To overcome the difficulty in obtaining fall data, this study proposes an algorithm that efficiently learns less data through machine learning such as KNN (k-nearest neighbors) and SVM (support vector machine). In addition, to improve the performance of these mathematical classifiers, this study utilized feature data aquired in the frequency space. The proposed algorithm analyzed the effect by diversifying the parameters of the model and the parameters of the frequency feature extractor through experiments using standard datasets. The proposed algorithm could adequately cope with a realistic problem that fall data are difficult to obtain. Because it is lighter than other classifiers, this algorithm was also easy to implement in small embedded systems where SIMD (single instruction multiple data) processing devices were difficult to mount.
Keywords
Fall detection; IMU sensors; Machine learning; Feature data;
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