Mobile IoT Barrier for Risk Area Protection
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Solution Overview
Problem
Users often fail to recognize risk conditions in their environment, leading to potential harm or injury, especially in cases where visual or auditory alerts are not acknowledged, such as by infants, toddlers, or individuals with impairments, necessitating a proactive barrier solution.
Innovation Solution
A computer-implemented method using IoT devices trained with machine learning models to recognize risk conditions and user movements, deploying mobile IoT devices as a barrier and generating distractions to divert users away from hazardous areas, with adjustments based on user behavior and movement patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual or auditory alerts are used to warn users of risk conditions, then users can be informed of potential hazards, but users such as infants, toddlers, or individuals with impairments fail to acknowledge or recognize these alerts
Solution Approach 1:
The system performs preliminary action by deploying mobile IoT devices to form a physical barrier before the user reaches the hazardous area. The machine learning model continuously monitors and predicts user movement patterns, activating the barrier proactively when approach is detected, rather than waiting for the user to acknowledge a warning first.
Solution Approach 2:
The mobile IoT devices serve as an intermediary between the risk condition and the user. Instead of relying on the user to process and respond to alerts, the system introduces a physical barrier that automatically intervenes to prevent contact with the hazard, mediating the interaction between user and risk.
2Reliability
If mobile IoT devices are deployed as a physical barrier, then user safety is enhanced by preventing contact with hazardous areas, but the system complexity and device coordination requirements increase
Solution Approach 1:
Multiple mobile IoT devices are merged to collectively form a single functional barrier. The devices operate as a coordinated unit under centralized control from the machine learning model, which manages their deployment, positioning, and maintenance as an integrated system rather than independent entities.
Solution Approach 2:
The barrier formed by mobile IoT devices is dynamic rather than static. The machine learning model continuously monitors user movement patterns and adjusts the barrier's position and configuration in real-time, enabling the system to adapt to changing conditions while maintaining safety.
3Reliability
If the barrier is dynamically adjusted to follow user movements, then continuous protection is maintained, but the energy consumption and operational complexity of the IoT devices increase
Solution Approach 1:
The machine learning model enables continuous monitoring and prediction of user movement patterns, allowing the barrier to maintain protective coverage without requiring constant physical adjustment. The system predicts future user positions and proactively positions the barrier accordingly, reducing the frequency of reactive movements.
Solution Approach 2:
The system implements feedback by continuously analyzing user movement patterns captured through video images and sensor data. The machine learning model processes this feedback to predict user trajectory and optimizes barrier positioning, creating a closed-loop control system that balances protection with energy efficiency.
Data Source
AI summary
A method generates a fence as a barrier between a user and an area of risk conditions within a designated space. One or more processors recognize an area of risk conditions, based a machine learning model trained by pre-determined indicators of the risk conditions and areas within the designated space. Movement patterns of a user are determined, based on machine learning of video images of the user's movements and behaviors. A risk condition is detected, based on the machine learning training of pre-determined areas and the pre-determined indicators of the risk conditions. Responsive to determining the user within or approaching the area of risk conditions, deploying, by the one or more processors, one or more mobile IoT devices as a fence between the user and the area of risk conditions, and generating a distraction diverting the user away from the area of the risk condition.