Pressure-Sensing Surface for Inferring Physical and Mental States
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Solution Overview
Problem
Current methods for detecting and tracking human or agent properties, behaviors, and intents rely heavily on non-contact visual data, which face challenges in accuracy and efficiency, especially in uncontrolled settings, and lack the ability to objectively predict physical and mental capacities, particularly for the elderly, and monitor changes in gait and balance over time.
Innovation Solution
A system that uses pressure contact patterns analyzed through a pressure-sensing surface to infer physical, behavioral, and mental states of objects and agents, employing machine learning algorithms to classify and predict changes in gait, balance, and intent, providing direct measurements and automating the process for improved accuracy and objectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If non-contact visual data methods are used to detect and track human properties, behaviors, and intents, then the system can operate in uncontrolled settings, but the accuracy and efficiency of detection deteriorates
Solution Approach 1:
The patent replaces non-contact visual sensing with direct contact pressure sensing. The pressure-sensing surface provides mechanical contact data that is inherently more accurate for detecting physical states like gait, balance, and posture. This substitution resolves the contradiction by using a different sensing modality (pressure instead of vision) that maintains high measurement precision while still operating in uncontrolled environments.
Solution Approach 2:
The pressure-sensing surface acts as an intermediary between the user and the detection system. Instead of directly observing users through cameras, the system mediates detection through pressure contacts that occur naturally during movement. This intermediary approach provides more reliable and accurate data about physical capacities while maintaining adaptability to uncontrolled settings.
2Measurement precision
If manual assessment methods are used to evaluate physical and mental capacities, then the system can provide expert judgment, but the cost and time consumption increases
Solution Approach 1:
The system enables self-service assessment where users are automatically evaluated as they naturally interact with the pressure-sensing surface during daily activities. No manual intervention or scheduling of assessment sessions is required. The system continuously collects pressure data and automatically infers physical and mental capacities, eliminating the time loss associated with manual expert assessments while maintaining measurement precision through automated machine learning analysis.
Solution Approach 2:
The system provides continuous assessment as users move across the pressure-sensing surface, rather than requiring discrete, scheduled evaluation sessions. This continuous collection and analysis of pressure data allows for ongoing monitoring of physical and mental capacities without the time consumption of repeated manual assessments, while maintaining expert-level accuracy through sophisticated inference algorithms.
3Reliability
If automated pressure sensing systems are implemented to monitor gait and balance, then the objectivity and efficiency of monitoring improves, but the device complexity increases
Solution Approach 1:
The pressure-sensing surface serves multiple functions: it detects gait patterns, balances, posture, and can infer both physical and mental capacities from the same pressure data. This multi-functionality reduces overall system complexity compared to having separate specialized devices for each measurement type, while maintaining high reliability and objectivity through comprehensive automated analysis.
4Loss of information
If pressure contact patterns are analyzed to infer mental states and cognitive capacity, then the ability to predict cognitive decline improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system uses automated machine learning analysis to replace difficult manual interpretation of pressure patterns. The algorithm automatically extracts cognitive state information from pressure contact data, making the detection and measurement of mental states more feasible while improving the ability to predict cognitive decline through objective, data-driven inference.
Data Source
AI summary
An apparatus for determining a non-apparent attribute of an object having a sensor portion with which the object makes contact and to which the object applies pressure. The apparatus has a computer in communication with the sensor portion that receives signals from the sensor portion corresponding to the contact and pressure applied to the sensor portion, and determines from the signals the non-apparent attribute. The apparatus has an output in communication with the computer that identifies the non-apparent attribute determined by the computer. A method for determining a non-apparent attribute of an object.


