Table 3 Comparative outcomes of IPOIAR-DPRNN methodology with existing methods18,19,25,26,27.

From: Enhancing indoor activity recognition for disabled persons using multi head self attention recurrent neural network with improved pelican algorithm

Methods

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

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

\(\:\varvec{R}\varvec{e}\varvec{c}{\varvec{a}}_{\varvec{l}}\)

\(\:{\varvec{F}}_{\varvec{m}\varvec{e}\varvec{a}\varvec{s}\varvec{u}\varvec{r}\varvec{e}}\)

Multi-part bag-of-poses

82.15

81.05

78.50

80.22

RF-PCA

89.67

82.71

81.84

76.02

HAR3DS-Lie Group

90.88

80.46

84.53

79.43

LMMML-Skelets

93.42

81.72

80.43

76.51

Lie group + CNN

93.00

81.96

83.40

77.83

Skeletal BoW

94.34

80.42

76.99

81.56

HDS-SP

95.88

76.29

77.34

84.50

SJACHA-3DCNN

96.76

85.26

80.88

83.01

YOLOv5

90.33

83.37

82.43

76.78

CNN

91.64

81.02

85.13

80.05

ConvLSTM

94.08

82.42

81.21

77.15

IPOIAR-DPRNN

97.11

87.10

86.94

86.98