---
language:
  - ko
  - en
tags:
  - transformer
  - video
  - audio
  - homecam 
  - multimodal
  - senior
  - yolo
  - mediapipe
---

# Model Card for `Silver-Multimodal`

<!-- Provide a quick summary of what the model is/does. -->

## Model Details

- The Silver-Multimodal model integrates both audio and video modalities for real-time situation classification. 
- This architecture allows it to process diverse inputs simultaneously and identify scenarios like daily activities, violence, and fall events with high precision. 
- The model leverages a Transformer-based architecture to combine features extracted from audio (MFCC) and video (MediaPipe keypoints), enabling robust multimodal learning.

- Key Highlights:
	- Multimodal Integration: Combines YOLO, MediaPipe, and MFCC features for comprehensive situation understanding.
	- Middle Fusion: The extracted features are fused and passed through the Transformer model for context-aware classification.
	- Output Classes:
	    - 0 Daily Activities: Normal indoor movements like walking or sitting.
	    - 1 Violence: Aggressive behaviors or physical conflicts.
	    - 2 Fall Down: Sudden fall or collapse.

![Multimodal Model](./pics/multimodal-overview.png)

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Activity with:** NIPA-Google(2024.10.23-20224.11.08), Kosa Hackathon(2024.12.9)
- **Model type:** Multimodal Transformer Model
- **API used:** Keras
- **Dataset:** [HuggingFace Silver-Multimodal-Dataset](https://huggingface.co/datasets/SilverAvocado/Silver-Multimodal-Dataset)
- **Code:** [GitHub Silver Model Code](https://github.com/silverAvocado/silver-model-code)
- **Language(s) (NLP):** Korean, English

## Training Details
### Dataset Preperation

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

- **HuggingFace:** [HuggingFace Silver-Multimodal-Dataset](https://huggingface.co/datasets/SilverAvocado/Silver-Multimodal-Dataset)
- **Description:**
   - The dataset is designed to support the development of machine learning models for detecting daily activities, violence, and fall down scenarios from combined audio and video sources.
    - The preprocessing pipeline leverages audio feature extraction, human keypoint detection, and relative positional encoding to generate a unified representation for training and inference.
	- Classes:
	    - 0: Daily - Normal indoor activities
	    - 1: Violence - Aggressive behaviors
	    - 2: Fall Down - Sudden falls or collapses

### Model Details

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

- **Model Structure:**
    ![Multimodal Model Structure](./pics/model-structure.png)

    - Input Shape and Division
	    1.	Input Shape:
            - The input shape for each branch is (N, 100, 750), where:
	            - N: Batch size (number of sequences in a batch).
	            - 100: Temporal dimension (time steps).
	            - 750: Feature dimension, representing extracted features for each input modality.
        2.	Why Four Inputs?:
            - The model processes four distinct inputs, each corresponding to a specific set of features derived from video keypoints. Here’s how they are divided:
            - Input 1, Input 2, Input 3:
                - For each detected individual (up to 3 people), the model extracts 30 keypoints using MediaPipe.
                - Each keypoint contains 3 features (x, y, z), resulting in 30 x 3 = 90 features per frame.
            - Input 4:
                - Represents relative positional coordinates calculated from the 10 most important key joints (e.g., shoulders, elbows, knees) for all 3 individuals.
                - These relative coordinates capture spatial relationships among individuals, crucial for contextual understanding.

    - Detailed Explanation of Architecture
	    1.	Positional Encoding:
	        - Adds temporal position information to the input embeddings, allowing the transformer to consider the sequence order.
	    2.	Multi-Head Attention:
	        - Captures interdependencies and relationships across the temporal dimension within each input.
	        - Ensures the model focuses on the most relevant frames or segments of the sequence.
	    3.	Dropout:
	        - Applies dropout regularization to prevent overfitting and improve generalization.
	    4.	LayerNormalization:
	        - Normalizes the output of each layer to stabilize training and accelerate convergence.
	    5.	Dense Layers:
	        - Extracts higher-level features after the attention mechanism.
	        - The first dense layer processes features from attention, followed by another dropout and dense layer to refine features further.
	    6.	AttentionPooling1D:
	        - Combines outputs from all four inputs into a unified representation.
	        - Aggregates temporal features using an attention mechanism, emphasizing the most important segments across modalities.
	    7.	Final Dense Layers:
	        - The combined representation is passed through dense layers and a softmax activation function for final classification into target classes:
	            - 0: Daily Activities
	            - 1: Violence
	            - 2: Fall Down

- **Model Performance:**
    ![Confusion Matrix](./pics/confusion-matrix.png)
    
    - Confusion Matrix Insights:
	    - Class 0 (Daily): 100% accuracy with no misclassifications.
	    - Class 1 (Violence): 96.96% accuracy with minimal false positives or false negatives.
	    - Class 2 (Fall Down): 98.67% accuracy, highlighting the model’s robustness in detecting falls.
	    - The overall accuracy is 98.37%, indicating the model’s reliability for real-time applications.
    

## Model Usage
- `Silver Assistant` Project
    - [GitHub SilverAvocado](https://github.com/silverAvocado)

## Load Model For Inference
```python
# Hugging Face Hub에서 모델 다운로드
MODEL_PATH="silver_assistant_transformer.keras"
model_path = hf_hub_download(repo_id="SilverAvocado/Silver-Multimodal", filename=MODEL_PATH)

# 사용자 정의 클래스 로드
model = load_model(
    model_path,
    custom_objects={
        "PositionalEncoding": PositionalEncoding,
        "AttentionPooling1D": AttentionPooling1D
    }
)

y_pred = np.argmax(model.predict([X_test1, X_test2, X_test3, X_test4]), axis=1)
accuracy = accuracy_score(y_test, y_pred)
print(f"Test Accuracy: {accuracy:.4f}")
```

## Conclusion
- The Silver-Multimodal model demonstrates exceptional capabilities in multimodal learning for situation classification. 
- Its ability to effectively integrate audio and video modalities ensures:
	1.	High Accuracy: Consistent performance across all classes.
	2.	Real-World Applicability: Suitable for applications like healthcare monitoring, safety systems, and smart homes.
	3.	Scalable Architecture: Transformer-based design allows future enhancements and additional modality integration.

- This model sets a new benchmark for multimodal AI systems, empowering safety-critical projects like `Silver Assistant` with state-of-the-art situation awareness.