Fall Risk Assessment Using Context-Aware Sensor Data

The patent uses sensors (accelerometers, gyroscopes, magnetometers) to capture movement data that copies the essential characteristics of clinical gait analysis. Instead of requiring patients to visit clinics for physical examinations, the system replicates assessment capabilities through wearable sensors that measure acceleration, orientation, and magnetic field changes during daily activities, providing objective fall risk data without clinical infrastructure.

The patent replaces mechanical clinical assessment systems with electronic sensor-based monitoring. Rather than clinicians physically observing and evaluating gait patterns, the system uses accelerometers, gyroscopes, and magnetometers to automatically capture and analyze movement mechanics, substituting human clinical judgment with automated sensor data processing and algorithmic fall risk assessment.

The patent enables continuous fall risk monitoring by having subjects wear sensors during daily activities rather than undergoing periodic clinic visits. The system continuously captures movement data across multiple dimensions (acceleration, orientation, magnetic field) and processes this data in real-time or near-real-time, providing ongoing assessment of gait quality, balance, and fall risk without interruption to the subject's normal life.

The system allows subjects to monitor their own fall risk independently without requiring clinician involvement for each assessment. The wearable sensors automatically capture movement data, and the embedded algorithms independently analyze gait patterns, balance metrics, and environmental interactions to generate fall risk assessments, enabling subjects to self-monitor and share data with caregivers or healthcare providers as needed.

The patent adds contextual dimensions to movement assessment by incorporating environmental sensors (light sensors, temperature sensors, humidity sensors) alongside movement sensors. Instead of merely measuring gait parameters in isolation, the system simultaneously captures environmental context data and uses this additional dimensional information to interpret movement patterns, distinguishing between variations caused by environmental factors versus those indicating genuine changes in fall risk.

The system dynamically adjusts assessment parameters and thresholds based on environmental context. When light sensors detect low-light conditions or temperature sensors detect extreme temperatures, the algorithm modifies expected movement patterns and adjusts fall risk thresholds accordingly. This allows the system to maintain measurement precision across varying home environments by adapting assessment criteria to contextual conditions rather than applying fixed thresholds.