Gait Evaluation Using Pressure and Limb Sensors for Fall Risk
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a need for a system that can accurately evaluate a user's gait in real time to identify normal, non-neuropathic, or neuropathic gaits to prevent falls, particularly among elderly populations and those with neurological diseases, as gait-related parameters can predict future falls and health conditions.
Innovation Solution
A gait evaluating system comprising a pressure detection mat, limb sensors, and a processor that analyzes step pressure values and limb motions to identify gait types and provides personalized feedback for muscle building, rhythmic training, or other interventions based on gait analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gait analysis is performed to identify gait types, then fall prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system divides gait analysis into multiple independent measurement modules: pressure detection mat for step pressure values, limb sensors for limb motion detection, and processor for feature extraction. Each module collects specific gait parameters independently, which are then integrated to identify gait types (normal, non-neuropathic, neuropathic), thereby achieving accurate classification without requiring a single complex system
Solution Approach 2:
The pressure detection mat serves multiple functions: detecting step pressure values, calculating step length, determining gait speed, and identifying gait types. The limb sensors similarly perform multiple functions including motion detection, joint angle measurement, and gait classification. This multi-functionality reduces overall system complexity while maintaining high measurement precision
2Reliability
If real-time gait monitoring is implemented, then fall prevention capability is improved, but energy consumption increases
Solution Approach 1:
The system automatically processes gait data and identifies gait types without requiring manual intervention. The processor continuously monitors step pressure values and limb motions, automatically extracts features, and classifies gait patterns in real-time. This self-service operation ensures continuous fall prevention capability while minimizing energy waste from manual operations
Solution Approach 2:
The pressure detection mat and limb sensors continuously monitor gait parameters without interruption, enabling real-time detection of gait changes. The system maintains continuous data collection and processing to identify gait types immediately when abnormalities occur, ensuring uninterrupted fall prevention capability
3Measurement precision
If multiple sensors are used for comprehensive gait analysis, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments gait measurement into distinct functional components: pressure detection mat for force and step characteristics, limb sensors for motion and posture analysis. Each sensor type focuses on specific gait parameters, and the processor integrates these segmented measurements to achieve comprehensive gait analysis with high accuracy
Solution Approach 2:
The system merges data from multiple sensors (pressure detection mat and limb sensors) through the processor to create a comprehensive gait profile. By combining step pressure values, limb motions, and derived features into a unified analysis, the system achieves measurement accuracy that would be impossible with any single sensor
Data Source
AI summary
A gait evaluating system including a processor is provided. The processor identifies whether a gait type of the user belongs to a normal gait, a non-neuropathic gait or a neuropathic gait based on step feature values of a user and walking limb feature values of the user. In response to that the gait type of the user belongs to the non-neuropathic gait, the processor controls the display panel to display a first auxiliary information, a second auxiliary information, and a third auxiliary information. The first auxiliary information indicates a potential sarcopenia of the user. The second auxiliary information indicates a dietary guideline for muscle building and muscle strengthening. The third auxiliary information shows a motion instruction video for regaining or maintaining muscle strength of the user.