• Smart Media Journal
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• v.10 no.3
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• pp.31-38
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• 2021

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.

• Smart Media Journal
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• v.12 no.2
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• pp.27-35
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• 2023

Currently, online user authentication is perform using joint certificates issued by accredited certification authorities and simple certificates issued by private agency. In such a PKI(Public Key Infrastructure) system, various cryptographic technologies are used, and in particular, digital signatures are used as a core technology. The digital signature scheme is equally used in DID(Decentralized Identity), which is attracting attention to replace the existing centralized system. As such, the digital signature-based user authentication used in current online services is also applied in the metaverse, which is attracting attention as the next-generation online world. Metaverse, a compound word of "meta," which means virtual and transcendent, and "universe," means a virtual world that includes the existing online world. Due to various developments of the metaverse, it is expted that new authentication technologies including biometric authentication will be used, but existing authentication technologies are still being used. Therefore, in this study, we study digital signature scheme that can be efficiently used for user authentication in the developing metaverse. In particular, we experimentally analyze the effectiveness of ECDSA, which is currently used as a standard for digital signatures, and Schnorr signatures, which can quickly verify a large amount of signatures.