## 3 Proposed Method
[57]. Figure 2: Architecture of MobileNetV2. To address this limitation, we adopted the specialized LSTM variant, Deep BiLSTM [58], an extension of BiLSTM [58; 59; 60]. This architecture excels in scrutinizing sequential spatial features within the local environment, capturing sustained patterns in spatial-temporal features. Figure 3 illustrates the detailed architecture of the Deep BiLSTM model, emphasizing its unique ability to comprehensively analyze and classify human actions based on the features extracted by MobileNetV2 [61]. The Deep BiLSTM model comes from LSTM [60; 61; 62], which is employed via three gates, namely the input gate, forget gate, and output gate. An LSTM unit can be precisely described as follows. \[i_{t}=\sigma(Y_{i}\cdot x_{t}+W_{i}\cdot h_{t-1}+b_{i}) \tag{1}\]

where \(Y\) and \(W\) denote the weight matrix, \(b\) signifies the bias term, \(i_{t}\) represents the input gate at time \(t\), \(\cdot\) denotes the matrix multiplication process, \(\sigma\) denotes the (sigmoid function), \(x_{t}\) signifies the input data at a time (\(t\)), and \(h_{t-1}\) signifies the output of the preceding LSTM unit. The input gate is crucial in identifying the specific information from the preceding unit that necessitates modification. \[f_{t}=\sigma(Y_{f}\cdot x_{t}+W_{f}\cdot h_{t-1}+b_{c}) \tag{2}\]

where \(f_{t}\) denotes the forget gate and is responsible for computing the significance of the information and forgetting old information. \[\tilde{c}_{t}=\tanh(Y_{c}\cdot x_{t}+W_{c}\cdot h_{t-1}+b_{c}) \tag{3}\]

\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\tilde{c}_{t} \tag{4}\]

Figure 3: Architectures of LSTM [61]. where the \(c_{t}\) state of the candidate is determined through the utilization of the tangent activation function, as illustrated in Equation (2). Subsequently, the current cell state is evaluated and provided in Equation, where \(\odot\) represents point-to-point multiplication. \[g_{t}=\sigma(Y_{g}x_{t}+W_{g}h_{t-1}+b_{g}) \tag{5}\]

\[h_{t}=g_{t}\odot tanh(c_{t}) \tag{6}\]

The output gate \(g_{t}\) is calculated in Equation (5), where \(h_{t}\) represents the output of the LSTM unit, as given in Equation (6). The baseline LSTM model predicts the current human activity based on only past data. It is evident that there is a possibility of losing certain information if the data are evaluated in a unidirectional manner. The Deep BiLSTM is made of two LSTM layers that operate in both the forward and backward directions, as depicted in Figure 4. \(v_{t}\) is the output layer in the Deep BiLSTM and is formulated as follows [59]. Figure 2:0

The forward and backward outcomes of the LSTM units are represented by the symbols \(\overleftarrow{h_{t}}\) and \(\overrightarrow{h_{t}}\). The combination of these two LSTM units forms the output \(v_{t}\). The fundamental concept underlying the RNN resides in the notion that, rather than transmitting the complete set of input data to the neural network in a singular instance, we progressively introduce the data one by one sequentially, effectively incorporating the temporal variable as well. If we possess an array of input values, we feed the initial value into the network and obtain a corresponding output. Subsequently, for the subsequent output, we feed the subsequent input alongside the preceding output. ## 4 Experimental Evolution and Performances

In this particular section, we evaluate the proposed approach on various benchmark datasets that are commonly used to assess action recognition. These datasets consisted of UCF11 or YouTube Actions [63], UCF Sport [64], and JHMDB [65]. The visual depiction of samples of different actions from each dataset can be found in the provided Figure 2:1

Figure 2:2 Framework diagram of Deep BiLSTM. ### Dataset

In the study, we used three human action datasets for the experiment. There are many datasets available, but we think these datasets are more challenging and need to work to improve recognition performance by solving challenges.