Computational Intelligence and Neuroscience / 2021 / Article / Tab 7

Research Article

An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering

Table 7

The CPU timing of the test case III by the AGD-SSC, AGD-SSC-NBC, IF, and LOF.

Seq. no.AGD-SSC
mean (st. dev.)
AGD-SSC-NBC
mean (st. dev.)
IF mean (st. dev.)LOF mean (st. dev.)

Seq. 11.3630 (0.0198)2.0991 (0.0188)10.3548 (0.0274)0.8779 (0.0093)
Seq. 20.9329 (0.0230)2.4315 (0.0703)10.3604 (0.0427)0.8888 (0.0249)
Seq. 31.2131 (0.0135)2.2532 (0.0214)10.3636 (0.0170)0.8875 (0.0222)
Seq. 41.6834 (0.0280)2.1603 (0.0303)10.3877 (0.1105)0.8844 (0.0228)
Seq. 51.1467 (0.0233)2.0710 (0.0313)10.3720 (0.0779)0.8873 (0.0290)
Seq. 61.0755 (0.0486)2.1199 (0.0306)10.4088 (0.2120)0.8901 (0.0459)
Seq. 71.3052 (0.0413)3.5250 (0.1036)10.3717 (0.0829)0.8869 (0.0287)
Seq. 81.0829 (0.0356)0.9906 (0.0323)10.3682 (0.0704)0.8866 (0.0271)
Seq. 91.3358 (0.0961)2.1020 (0.0131)10.3679 (0.0712)0.8900 (0.0336)
Seq. 101.1698 (0.0552)2.0080 (0.0292)10.3664 (0.0693)0.8884 (0.0264)

Average1.23082.276110.37210.8868