Table 5 Ablation study results of the VTSAMRNN-FARS methodology.

From: A vision transformer with recurrent neural network-based fall activity recognition system for disabled persons in smart IoT environments

Methods

\(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\)

\(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\)

\(\:\varvec{S}\varvec{e}\varvec{n}{\varvec{s}}_{\varvec{y}}\)

\(\:\varvec{S}\varvec{p}\varvec{e}{\varvec{c}}_{\varvec{y}}\)

VTSAMRNN-FARS

99.67

99.67

99.67

99.67

BiGRU-SAM

98.92

99.13

99.16

99.03

EWOA

98.28

98.61

98.62

98.28

ViT

97.64

97.90

98.02

97.74

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