Table 2 Indoor activity detection of MOEM-SMIADP model on HAR dataset.

From: Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities

Classes

Accuy

Precn

Recal

\(F{1}_{score}\)

\({G}_{Measure}\)

TRPH (70%)

 Walking

98.85

95.62

97.76

96.68

96.68

 Upstairs

98.73

95.74

95.74

95.74

95.74

 Downstais

98.50

95.20

93.60

94.39

94.39

 Sitting

98.76

96.50

96.01

96.26

96.26

 Standing

98.61

96.20

96.62

96.41

96.41

 Laying down

98.20

95.55

94.75

95.15

95.15

 Average

98.61

95.80

95.75

95.77

95.77

TSPH (30%)

 Walking

98.71

95.77

96.36

96.06

96.07

 Upstairs

98.75

95.31

96.17

95.74

95.74

 Downstais

98.51

95.68

94.18

94.93

94.93

 Sitting

98.94

97.48

96.36

96.92

96.92

 Standing

98.51

94.74

97.03

95.87

95.87

 Laying down

97.66

94.46

93.33

93.90

93.90

 Average

98.51

95.58

95.57

95.57

95.57