Personalized Kinetic State Models for Predictive Safety Monitoring
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Solution Overview
Problem
Current monitoring systems in custodial environments, such as hospitals and zoos, are inadequate in predicting and preventing unsafe situations, leading to injuries and incidents due to reliance on human vigilance and intuition, which is limited by familiarity and vigilance levels.
Innovation Solution
A system using kinesthetic activity sensors that collect audio, video, and physiological signals to develop personalized kinetic state models, predicting future behaviors and positional changes, and initiating notifications for potential unsafe outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human monitoring by security guards is used to predict future problems, then safety prediction capability is improved, but the system is limited by the vigilance and understanding of individual guards
Solution Approach 1:
The patent replaces the mechanical human monitoring system with an automated computer-based monitoring system that uses sensors, machine learning models, and algorithms to detect and predict unsafe situations. This substitution eliminates dependence on individual guard vigilance and understanding while maintaining or improving safety prediction capability through consistent, data-driven analysis.
Solution Approach 2:
The system enables self-monitoring and self-prediction of unsafe situations through automated algorithms that continuously analyze sensor data without requiring human intervention for basic detection functions. The machine learning models automatically adapt and improve their predictive capabilities over time based on accumulated data, making the system self-improving rather than dependent on human training and awareness.
2Measurement precision
If automated monitoring systems are implemented to predict unsafe situations, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the monitoring system into distinct modular components: sensor modules for data collection, processing modules for data analysis, machine learning modules for pattern recognition, and notification modules for alerting. This segmentation allows each component to be optimized independently while working together to achieve high prediction accuracy, and enables scalable deployment where not all components need to be implemented simultaneously.
Solution Approach 2:
The system introduces intermediate processing layers including data preprocessing modules, feature extraction modules, and model inference modules that bridge the gap between raw sensor data and final predictions. These intermediaries simplify the overall system architecture by breaking down complex analysis tasks into manageable stages, improving both accuracy and maintainability.
Data Source
AI summary
A system and a method for predicting kinesthetic outcomes from observed position, posture, behavior or activity of an individual 1602, 1702. The system uses kinesthetic activity sensors 102, 104 each collecting one or more of audio, video, or physiological signals and capturing the activity of the individual or an ambient environment of the individual. These signals are delivered into a computer system 106 implementing a learning routine 108 which constructs one or more personalized kinetic state models 1510 of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models 1500 for the individual and uses the multidimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future, and provides notice of predicted unsafe or undesired outcomes.