Open Access
ARTICLE
Reducing Operational Time Complexity of k-NN Algorithms Using Clustering in Wrist-Activity Recognition
Sun-Taag Choe, We-Duke Cho*, Jai-Hoon Kim, and Ki-Hyung Kim
Graduate School of Electrical and Computer Engineering, Ajou University
Suwon 443-749, Republic of Korea
* Corresponding Author: We-Duke Cho,
Intelligent Automation & Soft Computing 2020, 26(4), 679-691. https://doi.org/10.32604/iasc.2020.010102
Abstract
Recent research on activity recognition in wearable devices has identified a key
challenge: k-nearest neighbors (k-NN) algorithms have a high operational time
complexity. Thus, these algorithms are difficult to utilize in embedded wearable
devices. Herein, we propose a method for reducing this complexity. We apply a
clustering algorithm for learning data and assign labels to each cluster
according to the maximum likelihood. Experimental results show that the
proposed method achieves effective operational levels for implementation in
embedded devices; however, the accuracy is slightly lower than that of a
traditional k-NN algorithm. Additionally, our method provides the advantage of
controlling the computational burden, depending on the performance of the
embedded device on which the algorithm is implemented.
Keywords
Cite This Article
S. Choe, W. Cho, J. Kim and A. Ki-Hyung Kim, "Reducing operational time complexity of k-nn algorithms using clustering in wrist-activity recognition,"
Intelligent Automation & Soft Computing, vol. 26, no.4, pp. 679–691, 2020. https://doi.org/10.32604/iasc.2020.010102
Citations