Abstract
On-body device position awareness plays an important role relative to providing smartphone-based services with high levels of usability and quality. Existing on-body device localization methods deal with a fixed number of positions. In contrast, we have proposed a framework to discover new positions that are not initially supported by the system and add them as recognition targets during use. In this paper, we focus on the task of detecting new class candidates in the framework, which consists of anomaly detection, dimension reduction, and clustering. Anomaly detection and dimension reduction are preprocessing to make clustering more effective. A preliminary experiment is carried out to prove the concept and find out that it is appropriate to implement the k-means clustering on the number of clusters estimated by X-means after performing anomaly detection by IForest and dimension reduction by t-distributed stochastic neighbor embedding (t-SNE).
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This work is funded by the Japan Society for the Promotion of Science (JSPS) (Grant No. 18H03228).
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Saito, M., Fujinami, K. (2021). New Class Candidate Generation Applied to On-Body Smartphone Localization. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-15-8944-7_6
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DOI: https://doi.org/10.1007/978-981-15-8944-7_6
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