Computational Intelligence and Neuroscience / 2021 / Article / Tab 2

Research Article

An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering

Table 2

CCR/ACCR values for AGD-SSC, AGD-SSC-NBC, IF, and LOF in test case I.

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

Seq. 1CCR0.9138 (0.0000)0.9138 (0.0000)0.8897 (0.0105)0.8793 (0.0000)
ACCR0.9167 (0.0000)0.9167 (0.0000)0.5333 (0.0408)0.5000 (0.0000)

Seq. 2CCR0.9138 (0.0000)0.9132 (0.0031)0.9328 (0.0052)0.8621 (0.0000)
ACCR1.0000 (0.0000)1.0000 (0.0000)0.7583 (0.0250)0.4167 (0.0000)

Seq. 3CCR0.9828 (0.0000)0.9828 (0.0000)0.8822 (0.0064)0.8793 (0.0000)
ACCR0.9167 (0.0000)0.9167 (0.0000)0.5139 (0.0311)0.4167 (0.0000)

Seq. 4CCR0.9529 (0.0076)0.9500 (0.0081)0.8902 (0.0169)0.8793 (0.0000)
ACCR0.9167 (0.0000)0.9167 (0.0000)0.5389 (0.0826)0.5833 (0.0000)

Seq. 5CCR0.9310 (0.0000)0.8966 (0.0000)0.9408 (0.0139)0.8793 (0.0000)
ACCR0.9167 (0.0000)0.9167 (0.0000)0.7333 (0.0500)0.4167 (0.0000)

Seq. 6CCR0.9828 (0.0000)0.9310 (0.0000)0.8874 (0.0139)0.8793 (0.0000)
ACCR0.9167 (0.0000)0.9167 (0.0000)0.5694 (0.0573)0.5000 (0.0000)

Seq. 7CCR0.9644 (0.0043)0.9471 (0.0043)0.9052 (0.0097)0.9310 (0.0000)
ACCR0.9944 (0.0208)0.9944 (0.0208)0.6278 (0.0416)0.6667 (0.0000)

Seq. 8CCR0.9661 (0.0083)0.9103 (0.0103)0.9109 (0.0167)0.8793 (0.0000)
ACCR0.9167 (0.0215)0.9167 (0.0215)0.6722 (0.0643)0.5833 (0.0000)

Seq. 9CCR0.9828 (0.000)0.8966 (0.0000)0.8879 (0.0139)0.8966 (0.0000)
ACCR1.0000 (0.0000)1.0000 (0.0000)0.8889 (0.0393)0.7500 (0.0000)

Seq. 10CCR0.9638 (0.0052)0.9310 (0.0000)0.9511 (0.0141)0.8966 (0.0000)
ACCR1.0000 (0.0000)1.0000 (0.0000)0.7861 (0.0556)0.6667 (0.0000)

TotalCCR0.95540.92720.90780.8862
ACCR0.94940.94940.6610.5500