based of following information give PlantUML code for sequence diagram : 
The diagram you've provided illustrates a system for gesture and emotion recognition based on real-time video input, using machine learning and computer vision techniques. Here's a step-by-step description of the data flow in the system:

### 1. **Input Layer: Capturing Gestures**
   - **Camera Captures Gestures**: The system begins by capturing real-time input (video feed) of gestures and facial expressions using a camera.
   - This input is fed into the first processing layer where feature extraction begins.

### 2. **Feature Extraction via Computer Vision Tools**
   - **OpenCV**: The raw video frames or real-time input are processed using OpenCV, a popular open-source computer vision library. OpenCV extracts essential data points from the video, such as the location of key points that can define gestures or facial expressions.
   - **MediaPipe Holistic**: Following OpenCV, the system uses MediaPipe Holistic, which extracts precise (x, y, z) coordinates for various body parts such as hand gestures, facial landmarks, and pose data. This tool allows the system to track movements in three dimensions.

### 3. **Feature Extraction and Alignment**
   - **Feature Extraction**: The (x, y, z) coordinates captured are processed to extract specific features related to joint angles, velocities, and facial expression details. These features are crucial for analyzing both gestures and emotions.
   - **Align Facial and Pose Features with Hand Signs**: After extracting features, the system aligns data from different parts of the body—facial expressions, hand signs, and pose features—to create a cohesive representation of the user’s gesture or emotion.

### 4. **Sequential Gesture Analysis**
   - **LSTM (Long Short-Term Memory)**: The aligned feature data is then passed into an LSTM network. LSTMs are a type of recurrent neural network (RNN) designed to handle time-sequenced data. This is important for analyzing the dynamic nature of gestures that evolve over time, helping the system understand how gestures progress or change.
   - **GHMM (Gesture Boundary Refinement)**: The LSTM outputs are further processed using a Generalized Hidden Markov Model (GHMM), which refines the gesture boundaries and ensures that each segment of the movement is correctly identified as a specific gesture or pose.

### 5. **Gesture and Emotion Classification**
   - **Random Forest for Classification**: The refined gesture data and facial expression coordinates are passed to a Random Forest classifier. This machine learning algorithm uses multiple decision trees to classify the gesture and emotion based on the extracted features. Random Forest provides robust classification by averaging the predictions from various decision trees to increase accuracy.
   
   - The Random Forest receives:
     - **Facial Coordinates for Emotion**: This data is used specifically for emotion classification.
     - **Refined Gesture Data**: This is used for gesture classification.

### 6. **Output Layer**
   - **Classification Output**: The final classification of the gesture and emotion is generated. This output is then processed for display, either in text or as speech, based on the system's design.
   - **Text/Speech Display**: The recognized gesture and emotion are presented as feedback to the user, providing a final output in the form of text or speech.

### Summary of Flow:
1. **Input (video capture) -> Feature Extraction**: Camera captures video, and OpenCV + MediaPipe extract coordinates.
2. **Alignment**: The system aligns facial expressions with gesture features.
3. **Sequential Analysis (LSTM + GHMM)**: LSTM analyzes gesture sequence over time, while GHMM refines gesture boundaries.
4. **Classification (Random Forest)**: Both gestures and emotions are classified based on extracted features.
5. **Output (Text/Speech)**: The classified gestures and emotions are displayed to the user as feedback.

This approach enables the system to understand not only static gestures but also gestures in motion, along with associated emotional cues.