Ahmed A. M. Jamel1,∗, Bahriye Akay2,†
Computer Systems Science and Engineering, Vol.35, No.6, pp. 441-456, 2020, DOI:10.32604/csse.2020.35.441
Abstract Recently, owing to the capability of mobile and wearable devices to sense daily human activity, human activity recognition (HAR) datasets have become a
large-scale data resource. Due to the heterogeneity and nonlinearly separable nature of the data recorded by these sensors, the datasets generated require
special techniques to accurately predict human activity and mitigate the considerable heterogeneity. Consequently, classic clustering algorithms do not work
well with these data. Hence, kernelization, which converts the data into a new feature vector representation, is performed on nonlinearly separable data.
This study aims to present a robust method to… More >
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