3D Environment Risk Identification Using Reinforced Learning Agents
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Solution Overview
Problem
Traditional injury risk assessment in home environments is labor-intensive, time-consuming, and often occurs after an injury has taken place, posing risks to assessors and being limited in scalability and affordability, especially for the aging population who desire to maintain independence.
Innovation Solution
The implementation of a system using reinforced learning and deep learning to create a 3D model of a home environment, where an agent simulates user movements and interactions to identify potential injury risks, categorizing them as low-to-no risk or medium-to-high risk, enabling automated risk assessment without human exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional injury risk assessment methods are used, then assessors can identify risks in home environments, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent creates a digital 3D replica (copy) of the physical home environment using depth maps and object detection data. This virtual model allows automated risk assessment without requiring physical presence of assessors, thereby reducing time loss while maintaining assessment accuracy through comprehensive environmental analysis.
Solution Approach 2:
The patent replaces manual mechanical assessment processes with automated computer vision and machine learning systems. The agent autonomously navigates the environment, detects objects, and identifies risks using algorithms, substituting human labor with computational processes that operate faster and without fatigue.
2Measurement precision
If traditional assessment methods are deployed, then risks can be identified, but assessors are exposed to potential injuries and the process lacks scalability
Solution Approach 1:
The patent introduces an autonomous agent as an intermediary between the assessment system and the physical environment. This agent equipped with sensors and navigation capabilities performs risk assessment tasks remotely, eliminating direct human exposure to hazardous conditions while maintaining detection precision through sophisticated object recognition algorithms.
Solution Approach 2:
By creating a virtual representation of the environment, the system allows risks to be identified in the digital model without requiring human assessors to physically enter potentially dangerous situations. The copy enables safe remote analysis of environmental hazards.
3Measurement precision
If manual risk assessment is performed, then comprehensive environmental analysis is possible, but the process is not scalable and affordability is limited
Solution Approach 1:
The autonomous agent performs environmental scanning, object detection, and risk identification independently without requiring human intervention at each location. This self-service capability enables the system to autonomously conduct comprehensive assessments across multiple environments, dramatically improving scalability and reducing operational costs.
Solution Approach 2:
Replacing manual assessment mechanics with automated computer vision and machine learning enables parallel processing of multiple environments. The system can simultaneously analyze numerous homes or facilities, achieving scalability while maintaining comprehensive analysis through multi-sensor data collection and sophisticated risk algorithms.
Data Source
AI summary
Provided herein are exemplary methods for providing assessment of an injury risk, including deploying an agent into an environment, detecting a first object at a first state by the agent, taking an action to interact with the first object using reinforced learning by the agent, the action taken in order to maximize a reward, mapping the first object to a three-dimensional environment, and identifying potential risks within the three-dimensional environment. Also provided herein are exemplary systems for providing assessment of an injury risk, including an agent communicatively coupled to a sensing device, a communications network communicatively coupled to the agent, a three-dimensional model risk assessment module communicatively coupled to the communications network, a user device communicatively coupled to the three-dimensional model risk assessment module, and a system for identifying environmental risks communicatively coupled to the communications network.