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Fall Detection Model

Falling is one of the most dangerous events for seniors. It can cause serious injury and even death. Detecting falls is one of the primary purposes the wearable is designed for. An accurate model is one of the flagship features of the Golden Age Hub.

Foundation Checklist

Problem Definition

  • Clearly defined the specific problem to solve
  • Detect falls in real-time using wearable sensor data
  • Distinguish between actual falls and similar activities (e.g., sitting down quickly)
  • Minimize false alarms while ensuring high sensitivity

  • Provide immediate alerts to caregivers when falls are detected

  • Identified the type of ML task

  • Binary classification (fall vs. non-fall)
  • Time series analysis of sensor data
  • Real-time event detection with low latency requirements

  • (Future) Severity assessment of detected falls

  • Established clear success criteria

  • 95% sensitivity (minimize missed falls)

  • 90% specificity (minimize false alarms)

  • <500ms end-to-end detection latency
  • <5% battery impact from continuous monitoring

  • 90% caregiver confidence in alert accuracy

  • Determined if ML is actually the right approach

  • Traditional threshold methods have high false positive rates
  • ML can learn complex patterns in sensor data
  • Sufficient labeled fall data available for training
  • Edge deployment feasible with optimized models

Data and Assumptions

  • Inventoried all available data sources
  • 3-axis accelerometer (50-100Hz)
  • 3-axis gyroscope (50-100Hz)
  • Derived features (magnitude, jerk, etc.)
  • Annotated video recordings for ground truth

  • Self-reported fall incidents from users

  • Assessed data quantity and quality

  • 10,000+ labeled fall events across different scenarios
  • 100,000+ non-fall activities of daily living (ADLs)
  • Data augmentation for rare fall types

  • Continuous data collection from pilot deployments

  • Documented key assumptions

  • Wearable device is properly positioned on the body
  • Most falls follow recognizable impact and post-fall patterns
  • Sensor data quality is sufficient for reliable detection

  • Users will charge devices regularly

  • Identified potential biases

  • Limited data for certain fall types (e.g., backward falls)
  • Potential underrepresentation of frail elderly
  • Cultural differences in movement patterns
  • Variability in sensor placement

Performance and Ethics

  • Selected appropriate evaluation metrics
  • Primary: Sensitivity/Recall (critical for safety)
  • Secondary: Specificity/Precision (reduce false alarms)
  • Tertiary: Latency (time-to-detection)

  • Additional: Model size, inference time, power consumption

  • Established deployment thresholds

  • Sensitivity: ≥95% (critical for safety)
  • Specificity: ≥90% (minimize false alarms)
  • Detection latency: <500ms end-to-end
  • Model size: <5MB for edge deployment

  • Inference time: <100ms on target hardware

  • Ethical considerations

  • Risk: False negatives could delay medical attention
    • Mitigation: Multi-modal confirmation (audio, motion patterns)
  • Risk: Privacy concerns with continuous monitoring
    • Mitigation: On-device processing, data minimization

  • Risk: Over-reliance on technology

    • Mitigation: Clear user guidelines, not a replacement for human care

  • Fairness and bias mitigation

  • Tested across age groups (65-90+)
  • Validated on different body types and mobility levels
  • Considered cultural differences in movement

  • Regular bias audits with diverse test groups

  • Interpretability and explainability

  • SHAP values for feature importance
  • Attention mechanisms in model architecture
  • Clear documentation of model limitations
  • Fall confidence scores with explanations

Personalized Model Training

Data Collection Strategy

  • Passive Data Collection
  • Continuous background collection of sensor data (accelerometer, gyroscope)
  • Device orientation and movement patterns during normal activities
  • Environmental context (time of day, location, recent activity)

  • Battery-optimized sampling rates (higher during high-movement periods)

  • Active Data Collection

  • Periodic "activity challenges" with caregiver assistance
  • Simulated falls in controlled environments (with professional supervision)
  • User-initiated "near miss" reporting
  • Integration with medical alert systems for confirmed fall events

Labeling Strategy

  • Automated Initial Labeling
  • Threshold-based pre-labeling of potential fall events
  • Time-series anomaly detection for unusual movement patterns

  • Cross-validation with multiple sensor modalities

  • Human-in-the-Loop Verification

  • Caregiver verification of detected events
  • Simple mobile app interface with video/image review (when available)
  • Binary confirmation: "Was this a fall?" with optional severity rating

  • Contextual questions about recovery time and assistance needed

  • Quality Control

  • Multi-rater agreement for ambiguous cases
  • Confidence scoring for automated labels
  • Periodic review of labeling consistency
  • Feedback loop for model improvement

Model Personalization

  • Initial Model
  • Pretrained on population-level data
  • General fall patterns and common false positives

  • Optimized for high sensitivity (minimize missed falls)

  • Personalization Process

  • Warm-up Period (14-30 days)

    • Collect baseline movement patterns
    • Establish individual activity profiles
    • No critical alerts during this phase

  • Incremental Learning

    • Update model with verified events
    • Adjust sensitivity based on user's movement patterns
    • Regular validation against new data

  • Continuous Adaptation

    • Online learning for gradual pattern changes
    • Seasonal adjustments (e.g., winter vs summer movement)
    • Health condition-aware updates

Addressing Challenges

  • Rare Event Collection
  • Synthetic data augmentation for rare fall types
  • Transfer learning from similar users

  • Physics-based simulation of falls

  • User-Friendly Labeling

  • Voice-based confirmation ("Alexa, I just fell")
  • Automatic detection of prolonged immobility

  • Integration with smart home sensors for context

  • Data Quality

  • Sensor calibration checks
  • Signal quality monitoring
  • Automatic detection of device misplacement

Maintenance Planning

  • Performance monitoring
  • Real-time monitoring of prediction confidence
  • Weekly performance reports (sensitivity, specificity, F1)
  • User feedback collection and analysis

  • Alert system for performance degradation

  • Concept drift handling

  • Continuous monitoring of feature distributions
  • Statistical tests for data drift detection
  • Shadow deployment of new models

  • A/B testing framework for model updates

  • Established retraining frequency and triggers

  • Scheduled: Quarterly full retraining
  • Event-based: When new fall patterns are identified
  • Performance-based: When metrics drop below thresholds

  • Data-based: When significant new labeled data is available

  • Defined roles and responsibilities for model maintenance

  • ML Engineers: Model updates and validation
  • Data Scientists: Performance analysis and improvement
  • Clinical Staff: Ground truth verification

  • Product Team: User feedback integration

  • Documented procedures for model updates and versioning

  • Semantic versioning for model releases
  • Rollback procedures for failed updates
  • Change log with performance impact analysis
  • Compliance documentation for regulatory requirements