## 3 Proposed Method
```
Input: List of videos (dynamic human activity dataset) Corresponding ground truth labels (groundTruthLabels) Output: Prediction for the individual video Step 1: Preprocessing while\(Input\)Videos\(\
eq\)eachVideodo \(SequenceOframes\)\(\leftarrow\)VideosToImageConvertion(Video)  PreprocessedVideos.append(SequenceOframes)
```  
Step 2: Feature extraction with pre-trained models \(VideoFeatures\)\(\leftarrow\)\(loadModels(\)MobileNetV2) ```
Step 3: Classification \(ClassModels\)\(\leftarrow\)\(DeepBiLSTM(VideoFeature))\) Step 4: Generate predictions while\(i\
eq NumEpochs\)do // For training while\(BatchVideoFeatures\
eq NumberBatchTraining\)do \(PredictedClass\)\(\leftarrow\)\(ClassModels(BatchVideoFeatures)\) \(Loss\)\(\leftarrow\)\(Criterion(PredictedClass,groundTruthLabels)\) \(Updatethless\)\(\leftarrow\)\(Loss.backward(),Optimizer.Step()\) // For testing while\(BatchVideoFeatures\
eq NumberBatchTesting\)do \(PredictedClass\)\(\leftarrow\)\(ClassModels(BatchVideoFeatures)\) \(Output\)\(\leftarrow\)\(PerformanceMatrix(PredictedClass,groundTruthLabels)\) Step 5: Display results \(print(^{*}\)\(Performance.^{*},accuracy)\) Step 6: Real-time testing \(HumanActivityTestVideo\)\(\leftarrow\)\(Recorded(Video)\) \(PredictedActivityName\)\(\leftarrow\)\(ClassModels(HumanActivityTestVideo)\) \(print(^{*}\)Predicted:\(^{*},ActionName)\)
```

**Algorithm 1** Transfer-Learning-Feature-Based HAR

### Feature Extraction with MobileNetV2

The MobileNet, employed for feature extraction in our framework, is a compact, low-latency, and energy-efficient CNN model designed to empower deep neural network

Figure 1: Proposed method architecture. utilization on mobile devices [56]. Utilizing depthwise separable convolutions, it innovatively replaces traditional convolutions, dividing the process into depthwise and pointwise convolutions, significantly reducing the parameters and optimizing the size and computational efficiency. MobileNetV2's user-friendly characteristic notably allows users to adjust the network's magnitude and dimensions, catering to diverse computational needs. In our context, this facilitates feature extraction on both low-power and larger systems. The enhanced MobileNetV2 introduces a linear bottleneck and skipconnections, contributing to improved performance while maintaining accuracy [56]. The subsequent incorporation of the LSTM model replaces the softmax unit in our specific objective for predictions. Figure 2 demonstrates the working procedure of the MobileNetV2 architecture, which was designed by its efficient design, which revolves around inverted residual blocks. These models consisted of the depthwise and pointwise convolutional layer, which projects, also known as the projection layer. The idea behind this is to achieve a harmonious balance between computational efficiency and model performance. The utilization of linear bottlenecks and ReLU-6 activations in MobileNetV2 ensures streamlined operations while preserving the representational power of the model. By employing an initial 3 \(\times\) 3 convolutional layer for feature extraction and global average pooling for information aggregation, MobileNetV2 generates classification probabilities using dense layers. The adaptability of MobileNetV2, which allows for customization through the use of a width multiplier and resolution adjustments, positions it as an ideal choice for real-time computer vision tasks on devices with limitations in computational resources. Using this model, we produced effective features from the dynamic-human-action-based dataset. ### Classification with Deep BiLSTM

However, regarding the notion that extracting human activity recognition (HAR) features solely with CNN MobileNetV2 [56] is insufficient, we leveraged the power of Recurrent Neural Networks (RNNs), specifically the long short-term memory (LSTM) architecture. While RNNs exhibit proficiency in extracting temporal knowledge, the inherent challenge of mitigating exploding gradient problems limits their effectiveness over extended durations, as demonstrated by [PERSON] et al.