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An optimized hybrid deep learning model using ensemble learning... Table 7 Comparison with state of the art models

Table 7 An optimized hybrid deep learning model using ensemble learning... Table 7 Comparison with state of the art models

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An optimized hybrid deep learning model using ensemble learning...
An optimized hybrid deep learning model using ensemble learning...
Fig. 2 Different gait sub-phases event
Fig. 2 Different gait sub-phases event
RHC - Right heel contact , LTO - Left toe off, LHC- Left heel contact, ICC- Initial contact of the contralateral limb, RTO — Right toe off,SSL - Swimming limbs opposite to stance limb,VT- Vertical tibia, TS- Terminal swing.
RHC - Right heel contact , LTO - Left toe off, LHC- Left heel contact, ICC- Initial contact of the contralateral limb, RTO — Right toe off,SSL - Swimming limbs opposite to stance limb,VT- Vertical tibia, TS- Terminal swing.
Fig.4 Working flowchart of the proposed activities recognition system
Fig.4 Working flowchart of the proposed activities recognition system
-ig.5 Proposed hybrid deep learning architecture
-ig.5 Proposed hybrid deep learning architecture
Fig. 7 Accuracy classification report
Fig. 7 Accuracy classification report
Accuracy meanwhile takes a constant curve. Figure 9 shows the confusion matrix and accuracy and loss curve using LSTM-—CNN model. Output of CNN-GRU takes almost similar pattern as GRU-CNN, while this model gives us 100% accuracy. We can see the confusion matrix Fig. 10 and the characteristic plot Fig. 11. The ensemble learning model is best suited for the activ- ity recognition problem. Figure 12 shows the best output for ensemble learning model. When evaluating the classification models, it is required to test their accuracy
Accuracy meanwhile takes a constant curve. Figure 9 shows the confusion matrix and accuracy and loss curve using LSTM-—CNN model. Output of CNN-GRU takes almost similar pattern as GRU-CNN, while this model gives us 100% accuracy. We can see the confusion matrix Fig. 10 and the characteristic plot Fig. 11. The ensemble learning model is best suited for the activ- ity recognition problem. Figure 12 shows the best output for ensemble learning model. When evaluating the classification models, it is required to test their accuracy
Fig.8 CNN-LSTM model
Fig.8 CNN-LSTM model
An optimized hybrid deep learning model using ensemble learning...
An optimized hybrid deep learning model using ensemble learning...
(b) Confusion matrix for LSTM-CNN model.
(b) Confusion matrix for LSTM-CNN model.
(b) Confusion matrix for CNN-LSTM model.
(b) Confusion matrix for CNN-LSTM model.
Fig. 10 CNN-GRU model
Fig. 10 CNN-GRU model
(b) Confusion matrix for GRU-CNN model.
(b) Confusion matrix for GRU-CNN model.
Fig. 12 Ensemble learning model An optimized hybrid deep learning model using ensemble learning...
Fig. 12 Ensemble learning model An optimized hybrid deep learning model using ensemble learning...
Fig. 13 Individual activity accuracy classification report
Fig. 13 Individual activity accuracy classification report
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Machine LearningHumanoid RoboticsHuman Activity Recognition
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