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System Architecture

This document outlines the architecture of the UMass Shimmer sensor platform, including hardware components, software layers, and data flow.

Overview

The UMass Shimmer system follows a modular architecture designed for flexibility, scalability, and ease of integration with existing research workflows.

graph TB
    A[Shimmer Sensors] --> B[Docking Station]
    B --> C[Data Processing Layer]
    C --> D[Analysis Engine]
    D --> E[Visualization Layer]
    E --> F[Research Applications]

    G[Mobile App] --> C
    H[Web Interface] --> C
    I[API Gateway] --> C

Hardware Architecture

Shimmer Sensor Nodes

Core Components: - MCU: Low-power microcontroller for sensor management - Radio: Bluetooth Low Energy for wireless communication
- Sensors: Configurable sensor array (accelerometer, gyroscope, magnetometer, etc.) - Storage: Local data buffering and logging capability - Power: Rechargeable battery with power management

Docking Platform

Physical Interface: - Multi-sensor charging and data transfer - USB and Ethernet connectivity - LED status indicators - Compact desktop form factor

Functionality: - Simultaneous charging of multiple sensors - High-speed data download - Firmware updates - Configuration management

Software Architecture

Data Processing Pipeline

graph LR
    A[Raw Sensor Data] --> B[Data Validation]
    B --> C[Calibration]
    C --> D[Signal Processing]
    D --> E[Feature Extraction]
    E --> F[Data Storage]

Application Layers

1. Device Layer

  • Firmware: Embedded software on Shimmer devices
  • Drivers: Hardware abstraction and control
  • Protocols: Communication and data transfer standards

2. Middleware Layer

  • Connection Management: Device discovery and pairing
  • Data Synchronization: Real-time and batch data handling
  • Protocol Translation: Format conversion and standardization

3. Application Layer

  • Research Tools: Data analysis and visualization
  • Mobile Apps: Real-time monitoring and control
  • Web Interface: Dashboard and configuration management

4. Integration Layer

  • APIs: RESTful and WebSocket interfaces
  • SDKs: Python, MATLAB, and other language bindings
  • Plugins: Integration with existing research platforms

Data Flow

Real-time Streaming

  1. Sensor Activation: Configured sensors begin data collection
  2. Local Processing: Basic filtering and compression on-device
  3. Wireless Transmission: Bluetooth streaming to receiving device
  4. Data Reception: Host application receives and validates data
  5. Processing Pipeline: Real-time analysis and visualization

Batch Processing

  1. Data Logging: Sensors store data locally during collection
  2. Docking Transfer: High-speed download via docking station
  3. Data Validation: Integrity checking and error correction
  4. Analysis Pipeline: Offline processing and analysis
  5. Result Generation: Reports, visualizations, and exports

Security Architecture

Device Security

  • Encryption: AES-256 for data transmission
  • Authentication: Device pairing and access controls
  • Firmware Verification: Signed firmware updates

Data Security

  • Privacy: Personal data protection and anonymization
  • Storage: Encrypted local and cloud storage options
  • Access Control: Role-based permissions and audit logging

Scalability Considerations

Horizontal Scaling

  • Multi-device Support: Simultaneous operation of multiple sensors
  • Distributed Processing: Load balancing across processing nodes
  • Cloud Integration: Scalable cloud-based analysis infrastructure

Vertical Scaling

  • Performance Optimization: Efficient algorithms and data structures
  • Resource Management: Dynamic allocation of computing resources
  • Caching Strategy: Intelligent data caching for improved performance

Integration Points

Research Platforms

  • MATLAB: Native toolbox integration
  • Python: Comprehensive SDK and libraries
  • R: Statistical analysis packages
  • LabVIEW: Hardware interface modules

External Systems

  • EMR Integration: Healthcare record systems
  • LIMS: Laboratory information management
  • Cloud Platforms: AWS, Azure, Google Cloud integration
  • Analytics Tools: Tableau, PowerBI, Jupyter notebooks

Performance Metrics

Latency

  • Streaming: <100ms end-to-end latency
  • Processing: Real-time analysis capabilities
  • Response: <1s user interface responsiveness

Throughput

  • Data Rate: Up to 1MB/s per sensor
  • Concurrent Users: 100+ simultaneous connections
  • Batch Processing: 1GB+ datasets in minutes

Reliability

  • Uptime: 99.9% system availability
  • Data Integrity: <0.001% data loss rate
  • Error Recovery: Automatic retry and failover mechanisms