Load Sensor Mobility Assessment for Timely Fall Risk Detection
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Solution Overview
Problem
Existing mobility assessments in care facilities are manually performed by caregivers, which is time-consuming and may not promptly detect changes in patient mobility levels, increasing the risk of falls and associated healthcare costs.
Innovation Solution
A system utilizing load sensors and machine-learning algorithms to automatically evaluate patient mobility, generating a mobility score that can be integrated with electronic medical records to assess fall risk, and trigger alerts when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mobility assessments are performed by caregivers, then assessment accuracy can be maintained through direct observation, but caregiver workload increases and detection timeliness decreases
Solution Approach 1:
The patent replaces the manual mechanical observation system with an automated sensor-based detection system. Load sensors embedded in the patient support system automatically detect and record patient movements, eliminating the need for manual caregiver observation while maintaining assessment accuracy through continuous automated monitoring.
Solution Approach 2:
The patent introduces load sensors as an intermediary between the patient and the assessment system. These sensors indirectly measure patient mobility by detecting weight distribution changes during movement, providing accurate mobility data without requiring direct caregiver intervention.
2Loss of time
If continuous monitoring is implemented to detect mobility changes promptly, then fall risk detection timeliness improves, but system complexity increases
Solution Approach 1:
The patent makes the load sensors multi-functional by using them for both patient monitoring and mobility assessment. The same sensors that track patient presence and weight also detect mobility patterns through movement analysis, eliminating the need for separate dedicated mobility sensors and reducing overall system complexity.
Solution Approach 2:
The system performs self-assessment by automatically analyzing its own sensor data to detect mobility changes. The load sensor data is processed through algorithms that automatically identify movement patterns and assess mobility levels without requiring external intervention or complex additional monitoring equipment.
Data Source
AI summary
Automatically evaluating a level of mobility of a patient using a machine-learning model and load sensors integrated into a patient support system. A machine-learning model implemented on a computing system analyzes the load sensor data to determine whether the patient has difficulty standing up. This automated analysis can replace existing mobility assessments that were previously performed by caregivers. The mobility assessment data generated by the computing system can be recorded in the patient's electronic medical record (EMR) and utilized as a factor in determining patient fall risk.