Postural Stability Detection Using Pressure Sensors and HMM
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Solution Overview
Problem
Existing methods struggle to accurately and efficiently determine postural stability outside of a lab environment, particularly in real-time, due to challenges in detecting and correcting postural instability caused by aging, injuries, or transitions from zero gravity environments, lack of exercise, or the use of assistive devices.
Innovation Solution
A system and method utilizing pressure sensors to acquire and analyze pressure data points, employing Hidden Markov Models and Bayesian segmentation to identify and predict postural states, providing real-time feedback and stability adjustments through wearable devices like shoes or handheld modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pressure sensors are used to detect postural stability in real-time outside the lab, then the accessibility and practicality are improved, but the measurement accuracy and reliability deteriorate due to environmental factors and movement variability
Solution Approach 1:
The patent segments the postural assessment into multiple discrete pressure data points collected over time, rather than relying on a single measurement. This allows the system to divide the continuous postural state into identifiable patterns through clustering algorithms, improving measurement reliability in real-world conditions by analyzing multiple segmented instances of postural behavior
Solution Approach 2:
The system performs preliminary clustering analysis on pressure data to establish reference postural states before making real-time stability assessments. By pre-identifying normal vs. abnormal postural patterns through unsupervised learning, the system prepares classification models in advance that can quickly and accurately evaluate current postural state without requiring complex real-time computation
2Reliability
If multiple pressure data points are collected over time to improve postural state identification, then the measurement reliability is improved, but the data processing complexity and time consumption increase
Solution Approach 1:
The system employs unsupervised clustering algorithms that automatically identify postural states without requiring manual labeling or complex predefined classification rules. The algorithm self-organizes the pressure data into meaningful patterns based on inherent data structures, reducing the need for complex processing pipelines while maintaining high reliability in postural state identification
Solution Approach 2:
The patent transforms raw pressure data into clustered postural state representations, changing the parameter space from continuous pressure values to discrete state categories. This parameter transformation simplifies subsequent analysis by converting complex multi-dimensional pressure information into manageable state labels that are easier to process and interpret
3Measurement precision
If traditional lab-based methods are used for postural stability detection, then the measurement control is improved, but the practical applicability in daily life deteriorates
Solution Approach 1:
The patent develops a pressure sensor system integrated into footwear that serves multiple functions: it monitors postural stability, detects gait patterns, and provides real-time feedback for fall prevention. This universal design allows the same device to function effectively both in controlled lab environments and in diverse real-world settings, bridging the gap between measurement precision and practical applicability
Data Source
AI summary
A method for determining postural stability of a person can include acquiring a plurality of pressure data points over a period of time from at least one pressure sensor. The method can also include the step of identifying a postural state for each pressure data point to generate a plurality of postural states. The method can include the step of determining a postural state of the person at a point in time based on at least the plurality of postural states.