Sun-Taag Choe, We-Duke Cho*, Jai-Hoon Kim, and Ki-Hyung Kim
Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 679-691, 2020, DOI: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… More >
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