@Article{iasc.2020.010102,
AUTHOR = {Sun-Taag Choe, We-Duke Cho, Jai-Hoon Kim, and Ki-Hyung Kim},
TITLE = {Reducing Operational Time Complexity of k-NN Algorithms Using  Clustering in Wrist-Activity Recognition},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {4},
PAGES = {679--691},
URL = {http://www.techscience.com/iasc/v26n4/40272},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2020.010102}
}