Lower Extremity Motion Screening for Continuous Frailty Detection
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Solution Overview
Problem
Current frailty analysis techniques require trained personnel and are limited to supervised environments, failing to detect long-term changes in lower extremity performance and the impact of treatment plans on mobility.
Innovation Solution
A computerized platform analyzes motion data from lower extremities using sensors integrated into wearables or implants to determine gait characteristics, enabling continuous monitoring and risk assessment outside controlled environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current frailty analysis techniques using trained personnel are used, then assessment accuracy is improved, but device complexity and operational requirements increase
Solution Approach 1:
The patent replaces the mechanical/visual assessment system performed by trained personnel with an automated sensor-based motion analysis system. Sensors capture gait parameters objectively, and algorithms process this data to detect frailty indicators, eliminating the need for human observers while maintaining or improving measurement precision through consistent, repeatable data collection.
Solution Approach 2:
The system enables self-assessment through automated motion capture and analysis. The sensors and processing algorithms work autonomously to evaluate gait characteristics and detect frailty indicators without requiring trained personnel to perform the assessment, making the system self-sufficient and easier to deploy.
2Reliability
If supervised environment assessments are conducted, then measurement reliability is improved, but loss of time and operational flexibility worsen
Solution Approach 1:
The system enables continuous gait monitoring through wearable or embedded sensors that collect motion data during natural daily activities. This continuous data collection provides reliable frailty assessment information without requiring scheduled supervised assessment sessions, eliminating time loss while maintaining reliability through ongoing measurement.
Solution Approach 2:
The system performs preliminary frailty detection through automated analysis of gait parameters, identifying at-risk individuals before comprehensive clinical assessments are needed. This preliminary screening occurs continuously in the background, providing reliable early warnings without requiring time-consuming supervised evaluation sessions.
3Measurement precision
If controlled environment monitoring is used, then data quality is improved, but adaptability to real-world conditions worsens
Solution Approach 1:
The sensor system is designed to function universally across multiple environments and conditions. The same sensors and algorithms that provide precise gait measurement in controlled settings also operate effectively in real-world diverse environments, enabling the system to adapt to various walking surfaces, lighting conditions, and activity contexts while maintaining data quality.
Solution Approach 2:
The system adjusts measurement parameters and analysis thresholds based on environmental context and individual baseline characteristics. By dynamically adapting parameters such as gait speed ranges, step length expectations, and propulsion phase definitions to match real-world conditions, the system maintains measurement precision across diverse settings rather than requiring controlled environments.
Data Source
AI summary
Lower extremity motion data may be received from one or more sensors. The lower extremity motion data may be used to quantify propulsion performance, which is used to calculate one or more gait characteristics of the lower extremity. A risk level of the lower extremity may be determined based, at least in part, on the calculated gait characteristics.