Computer Vision Physical Compatibility Assessment
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Solution Overview
Problem
Current systems lack the capability to assess and classify the physical compatibility of two or more individuals to collectively perform desired physical acts or activities, and project future capabilities, especially when considering anatomical variations and the use of assistive devices.
Innovation Solution
The development of systems and methods using computer vision and statistical/neural network-based approaches to measure and classify physical compatibility by acquiring images of individuals, determining anatomical measurements, and projecting future capabilities based on growth patterns and medical conditions, while also considering the use of assistive devices and objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision and neural network-based approaches are used to measure physical dimensions and assess collective abilities, then measurement precision and classification accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system employs a multi-functional integrated approach where camera-based computer vision technology performs multiple functions including capturing images, measuring physical dimensions, assessing collective abilities, and projecting future capabilities. This universal system consolidates what would otherwise require multiple separate devices, thereby improving measurement precision without proportionally increasing device complexity
Solution Approach 2:
The patent replaces traditional mechanical measurement devices with optical-based computer vision systems. Instead of using physical calipers, rulers, or anthropometric tools, the system uses cameras and neural networks to automatically capture and analyze physical dimensions, significantly improving measurement precision while reducing the mechanical complexity of the assessment apparatus
2Reliability
If the system projects future capabilities based on growth patterns and medical conditions, then the ability to predict future physical compatibility is improved, but the complexity of data processing and modeling increases
Solution Approach 1:
The system performs preliminary assessments of current physical dimensions and growth patterns to predict future capabilities before actual future events occur. By analyzing current anthropometric data, growth trajectories, and medical conditions, the system proactively projects future physical compatibility, enabling informed decisions about collective activities in advance rather than requiring complex real-time analysis later
Solution Approach 2:
The system dynamically adjusts prediction models based on changing parameters such as age, growth patterns, and medical conditions. By incorporating these variable parameters into the neural network-based classification, the system improves prediction reliability for future capabilities while managing data processing complexity through structured parameter integration rather than requiring analysis of all possible variables
3Adaptability or versatility
If the system considers assistive devices and objects in assessing collective abilities, then adaptability and comprehensiveness of assessment are improved, but the complexity of evaluating interactions increases
Solution Approach 1:
The system introduces assistive devices and objects as intermediary elements in the assessment process. Rather than directly assessing only human-human interactions, the system evaluates how assistive devices mediate between individuals to enable collective activities. This intermediary approach increases assessment versatility by accommodating diverse physical capabilities while managing interaction complexity through structured evaluation of device-mediated tasks
4Measurement precision
If high-resolution 3-dimensional scanning devices are used for medical anthropometry, then measurement precision is improved, but the cost and accessibility of the system worsens
Solution Approach 1:
The system creates digital copies of physical dimensions using camera-based imaging instead of requiring physical contact with expensive 3D scanning devices. By capturing images and using computer vision algorithms to extract anthropometric measurements, the system produces accurate digital representations of physical dimensions at a fraction of the cost and complexity of medical-grade 3D scanners, thereby improving accessibility while maintaining measurement precision
Solution Approach 2:
The patent substitutes expensive mechanical 3D scanning systems with optical camera-based measurement systems. Instead of using complex mechanical sensors and contact-based measurement tools, the system uses standard or consumer-grade cameras combined with neural network analysis to achieve accurate anthropometric measurements, dramatically improving system accessibility and reducing deployment costs
Data Source
AI summary
Systems and methods are described for measuring, using one or more cameras, anthropomorphic features of two or more individuals to classify and/or grade whether the two or more individuals possess physical abilities to collectively perform desired physical acts, actions or activities. The performance of such actions may involve objects with measured or known dimensions, and/or one or more assistive devices to compensate for differences in anthropomorphic features. Anthropomorphic features of the two or more individuals may also be projected into the future based on attributes such as ages, medical conditions, activity levels and predispositions. Classification schemes may use neural network-based approaches and/or statistical methods based on labelled datasets of abilities of two or more individuals with given anthropomorphic features to perform selected actions.