TY - EJOU AU - Choe, Sun-Taag AU - Cho, We-Duke AU - Kim, Jai-Hoon AU - Kim, and Ki-Hyung TI - Reducing Operational Time Complexity of k-NN Algorithms Using Clustering in Wrist-Activity Recognition T2 - Intelligent Automation \& Soft Computing PY - 2020 VL - 26 IS - 4 SN - 2326-005X AB - 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. KW - Embedded Wearable Device KW - Human-Activity Recognition KW - Instance Reduction KW - k-Nearest Neighbors KW - k-Means Clustering KW - Triaxial Signal DO - 10.32604/iasc.2020.010102