Golden Age Hub - AI/ML Design Document¶

To preserve patient privacy and eliminate unnecessary cloud transit costs, Golden Age Hub deploys quantized machine learning models directly to edge patches. This document describes the model architectures, dataset parameters, edge inference pipelines, and federated learning strategies used to detect falls and identify wandering patterns.

2. Model Architecture¶

2.1 Fall Detection Model¶

Input Features:

• 3-axis accelerometer data (50-100Hz)
• 3-axis gyroscope data (50-100Hz)
• Derived features (magnitude, jerk, etc.)
• Temporal context (1-2 second windows)

Architecture:

Input Layer (6-12 features × 50-100 timesteps)
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1D Convolutional Layer (64 filters, kernel=5, relu)
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Batch Normalization
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MaxPooling1D (pool_size=2)
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Bidirectional LSTM (64 units)
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Dropout (0.5)
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Dense (32, relu)
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Output Layer (sigmoid)

2.2 Activity Recognition¶

Activities Classified:

• Lying
• Sitting
• Standing
• Walking
• Stairs (up/down)
• Running
• Falling

Model Architecture:

• Similar to fall detection but with softmax output
• Multi-class classification
• Class weights for imbalanced data

3. Federated Learning Architecture¶

3.1 System Overview¶

• Central Server: Aggregates model updates, maintains meta-models
• Edge Hubs: Local model training with private data
• Secure Aggregation: Protects individual data privacy
• Differential Privacy: Adds noise to model updates

3.2 Model Initialization¶

1. Meta-Model Training:

2. Trained on aggregated, anonymized dataset

3. Serves as starting point for personalization

4. Updated periodically with federated learning

5. Edge Deployment:

6. Meta-model deployed to edge devices
7. Local fine-tuning with user data
8. Secure update sharing with central server

3.3 Training Workflow¶

# ZenML pipeline for federated learning
@pipeline
def federated_training_pipeline():
    # Load latest meta-model
    meta_model = load_meta_model()

    # Local training on edge device
    local_data = load_local_data()
    local_model = train_local_model(meta_model, local_data)

    # Generate model update (differentially private)
    update = create_model_update(meta_model, local_model)

    # Secure aggregation (handled by central server)
    if is_online():
        submit_update(update)
        aggregated_model = fetch_aggregated_model()
        return aggregated_model
    return local_model

4. ML Ops with ZenML¶

4.1 Pipeline Components¶

• Data Validation: Ensure data quality and consistency
• Feature Engineering: Extract relevant features from sensor data
• Model Training: Local fine-tuning of meta-models
• Evaluation: Performance metrics and fairness checks
• Deployment: Model versioning and A/B testing

4.2 Model Monitoring¶

• Data Drift: Detect distribution shifts in sensor data
• Performance Metrics: Track precision, recall, F1-score
• Edge Device Health: Monitor model inference performance

5. Data Pipeline¶

3.1 Data Collection¶

• Synthetic Data Generation

• Physics-based simulation of falls

• Realistic sensor noise modeling

• Various body types and movement patterns

• Real-world Data Collection

• Controlled environment testing
• Annotated by multiple raters
• Diverse population representation

3.2 Preprocessing¶

1. Temporal Alignment

2. Resampling to consistent frequency

3. Handling missing values

4. Time synchronization across sensors

5. Feature Engineering

6. Time-domain features (mean, std, min, max, etc.)

7. Frequency-domain features (FFT, power spectral density)

8. Domain-specific features (e.g., free-fall detection)

9. Data Augmentation

10. Time warping
11. Adding noise
12. Sensor orientation changes
13. Speed variations

6. Model Training¶

8. Deployment Strategy¶

• Cross-validation: 5-fold stratified
• Class Weights: Address class imbalance
• Early Stopping: Patience=10, monitor validation loss
• Learning Rate: Reduce on plateau

4.2 Performance Metrics¶

• Fall Detection

• Precision/Recall (high recall is critical)

• False positive rate

• Detection latency

• Activity Recognition

• Per-class F1 score
• Confusion matrix
• Real-time inference speed

5. Edge Deployment¶

5.1 Model Optimization¶

• Quantization (FP16/INT8)
• Pruning
• Knowledge distillation
• TensorFlow Lite Micro conversion

5.2 On-device Inference¶

• Fixed-point arithmetic
• Memory optimization
• Power-efficient execution
• Model update mechanism

6. Continuous Learning¶

6.1 Feedback Loop¶

• False positive/negative reporting
• Edge case collection
• Model performance monitoring

6.2 Model Updates¶

• Secure OTA updates
• A/B testing
• Rollback mechanism
• Version compatibility

7. Privacy and Security¶

9.1 Federated Learning Security¶

• Secure Aggregation: Prevents reconstruction of individual updates
• Differential Privacy: Adds calibrated noise to model updates
• Encryption: End-to-end encryption for all model communications
• Access Control: Strict authentication and authorization

9.2 Data Minimization¶

• Raw sensor data stays on edge devices
• Only model updates (not raw data) are shared
• Automatic data retention policies

9.3 Compliance¶

• HIPAA-compliant data handling
• User consent management
• Audit logging for all data access

7.1 Data Privacy¶

• On-device processing
• Differential privacy
• Federated learning
• Secure aggregation

7.2 Model Security¶

• Model encryption
• Runtime protection
• Adversarial defense
• Integrity verification

7. Model Evaluation Protocol¶

8.1 Offline Evaluation¶

• Holdout test set
• Cross-dataset validation
• Ablation studies

8.2 Real-world Testing¶

• Pilot studies
• Longitudinal monitoring
• User feedback analysis

9. Future Directions¶

• Multi-modal fusion (camera, audio)
• Personalized models
• Predictive analytics
• Explainable AI features